fix ci pre-commit

Signed-off-by: yewentao256 <zhyanwentao@126.com>
[Core] Update PyTorch to 2.13.0, torchvision to 0.28.0, triton to 3.7.1 (#48155 )
2026-07-23 18:15:27 +00:00 · 2026-07-23 11:09:36 -07:00 · 2026-07-23 11:08:45 -07:00 · 2026-07-23 12:26:02 -05:00 · 2026-07-23 17:16:19 +00:00 · 2026-07-23 13:08:08 -04:00
387 changed files with 12588 additions and 4340 deletions
@@ -813,8 +813,8 @@ steps:

            # Download artifacts from current build
            echo "Downloading artifacts from current build"
-            # buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
-            # buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .
+            buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
+            buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .

            # # Run upload script
            bash .buildkite/scripts/upload-rocm-wheels.sh
@@ -17,7 +17,7 @@ DEFAULT_REPO_SLUG="vllm-project/vllm"
 DEFAULT_CI_HCL_SOURCE="docker/ci-rocm.hcl"
 DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tools/install_protoc.sh rust-toolchain.toml tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh .buildkite/scripts/rocm/build-ci-base.sh"
 DEFAULT_CI_BASE_DOCKERFILE="docker/Dockerfile.rocm"
-DEFAULT_CI_BASE_DOCKERFILE_STAGES="base rust_toolchain_input_0 rust_toolchain_input_1 rust-toolchain-input rust-toolchain build_rixl build_rocshmem build_deepep mori_base ci_base"
+DEFAULT_CI_BASE_DOCKERFILE_STAGES="base rust_toolchain_input_0 rust_toolchain_input_1 rust-toolchain-input rust-toolchain build_nixl build_rocshmem build_deepep mori_base ci_base"
 DEFAULT_CI_BASE_METADATA_VERSION="1"
 IMAGE_EXISTED_BEFORE_BUILD=0

@@ -1159,8 +1159,8 @@ ci_base_metadata_pairs() {
    metadata_pair "vllm.rocm.nic_backend" "$(resolve_dockerfile_arg_value "${dockerfile}" "NIC_BACKEND")"
    metadata_pair "vllm.rocm.ainic_version" "$(resolve_dockerfile_arg_value "${dockerfile}" "AINIC_VERSION")"
    metadata_pair "vllm.rocm.ubuntu_codename" "$(resolve_dockerfile_arg_value "${dockerfile}" "UBUNTU_CODENAME")"
-    metadata_pair "vllm.rocm.rixl_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "RIXL_REPO")"
-    metadata_pair "vllm.rocm.rixl_commit" "${RIXL_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "RIXL_BRANCH")}"
+    metadata_pair "vllm.rocm.nixl_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "NIXL_REPO")"
+    metadata_pair "vllm.rocm.nixl_commit" "${NIXL_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "NIXL_BRANCH")}"
    metadata_pair "vllm.rocm.ucx_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "UCX_REPO")"
    metadata_pair "vllm.rocm.ucx_commit" "${UCX_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "UCX_BRANCH")}"
    metadata_pair "vllm.rocm.rocshmem_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "ROCSHMEM_REPO")"
@@ -1169,7 +1169,7 @@ ci_base_metadata_pairs() {
    metadata_pair "vllm.rocm.deepep_commit" "${DEEPEP_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_BRANCH")}"
    metadata_pair "vllm.rocm.deepep_nic" "$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_NIC")"
    metadata_pair "vllm.rocm.deepep_rocm_arch" "$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_ROCM_ARCH")"
-    metadata_pair "vllm.rocm.rixl_cache_key" "${RIXL_CACHE_KEY:-}"
+    metadata_pair "vllm.rocm.nixl_cache_key" "${NIXL_CACHE_KEY:-}"
    metadata_pair "vllm.rocm.rocshmem_cache_key" "${ROCSHMEM_CACHE_KEY:-}"
    metadata_pair "vllm.rocm.deepep_cache_key" "${DEEPEP_CACHE_KEY:-}"

@@ -1686,7 +1686,7 @@ extract_dependency_pins() {
        return 0
    fi

-    for var in RIXL_BRANCH UCX_BRANCH ROCSHMEM_BRANCH DEEPEP_BRANCH; do
+    for var in NIXL_BRANCH UCX_BRANCH ROCSHMEM_BRANCH DEEPEP_BRANCH; do
        if [[ -n "${!var:-}" ]]; then
            echo "Using provided ${var}: ${!var}"
            continue
@@ -1706,30 +1706,30 @@ extract_dependency_pins() {
 compute_dependency_cache_keys() {
    local bake_dir=""
    local dockerfile_rocm=""
-    local rixl_branch=""
+    local nixl_branch=""
    local ucx_branch=""
    local rocshmem_branch=""
    local deepep_branch=""
-    local rixl_material=""
+    local nixl_material=""
    local rocshmem_material=""
    local deepep_material=""

    bake_dir=$(dirname "${VLLM_BAKE_FILE}")
    dockerfile_rocm="${bake_dir}/Dockerfile.rocm"
-    rixl_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "RIXL_BRANCH")
+    nixl_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "NIXL_BRANCH")
    ucx_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "UCX_BRANCH")
    rocshmem_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "ROCSHMEM_BRANCH")
    deepep_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "DEEPEP_BRANCH")

-    if [[ -n "${rixl_branch}" && -n "${ucx_branch}" ]]; then
-        rixl_material=$(compose_stage_cache_material "${dockerfile_rocm}" "base build_rixl")
-        RIXL_CACHE_KEY=$(
+    if [[ -n "${nixl_branch}" && -n "${ucx_branch}" ]]; then
+        nixl_material=$(compose_stage_cache_material "${dockerfile_rocm}" "base build_nixl")
+        NIXL_CACHE_KEY=$(
            compose_dependency_cache_key \
-                "${rixl_branch}-ucx-${ucx_branch}" \
-                "${rixl_material}"
+                "${nixl_branch}-ucx-${ucx_branch}" \
+                "${nixl_material}"
        )
-        export RIXL_CACHE_KEY
-        echo "RIXL dependency cache key: ${RIXL_CACHE_KEY}"
+        export NIXL_CACHE_KEY
+        echo "NIXL dependency cache key: ${NIXL_CACHE_KEY}"
    fi

    if [[ -n "${rocshmem_branch}" ]]; then
@@ -1780,11 +1780,11 @@ dependency_cache_ref_for_target() {
    local cache_repo="${DOCKERHUB_CACHE_REPO:-rocm/vllm-ci-cache}"

    case "${target}" in
-        rixl-rocm-ci)
-            if [[ -n "${RIXL_CACHE_KEY:-}" ]]; then
-                printf '%s\n' "${cache_repo}:rixl-rocm-${RIXL_CACHE_KEY}"
-            elif [[ -n "${RIXL_BRANCH:-}" ]]; then
-                printf '%s\n' "${cache_repo}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH:-}"
+        nixl-rocm-ci)
+            if [[ -n "${NIXL_CACHE_KEY:-}" ]]; then
+                printf '%s\n' "${cache_repo}:nixl-rocm-${NIXL_CACHE_KEY}"
+            elif [[ -n "${NIXL_BRANCH:-}" ]]; then
+                printf '%s\n' "${cache_repo}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH:-}"
            fi
            ;;
        rocshmem-rocm-ci)
@@ -1815,7 +1815,7 @@ add_dependency_cache_target() {

 resolve_ci_base_dependency_targets() {
    local mode="${ROCM_DEP_CACHE_EXPORT_MODE:-missing}"
-    local rixl_ref=""
+    local nixl_ref=""
    local rocshmem_ref=""
    local deepep_ref=""

@@ -1824,7 +1824,7 @@ resolve_ci_base_dependency_targets() {
    case "${mode}" in
        always)
            echo "ROCM_DEP_CACHE_EXPORT_MODE=always; exporting all dependency caches serially"
-            for target in rixl-rocm-ci rocshmem-rocm-ci deepep-rocm-ci; do
+            for target in nixl-rocm-ci rocshmem-rocm-ci deepep-rocm-ci; do
                if [[ -n "$(dependency_cache_ref_for_target "${target}")" ]]; then
                    add_dependency_cache_target "${target}"
                fi
@@ -1844,13 +1844,13 @@ resolve_ci_base_dependency_targets() {
            ;;
    esac

-    if [[ "${mode}" != "always" && -n "${RIXL_CACHE_KEY:-}" ]]; then
-        rixl_ref=$(dependency_cache_ref_for_target "rixl-rocm-ci")
-        if dependency_cache_ref_exists "${rixl_ref}"; then
-            echo "RIXL dependency cache exists: ${rixl_ref}"
+    if [[ "${mode}" != "always" && -n "${NIXL_CACHE_KEY:-}" ]]; then
+        nixl_ref=$(dependency_cache_ref_for_target "nixl-rocm-ci")
+        if dependency_cache_ref_exists "${nixl_ref}"; then
+            echo "NIXL dependency cache exists: ${nixl_ref}"
        else
-            echo "RIXL dependency cache missing; will seed: ${rixl_ref}"
-            add_dependency_cache_target "rixl-rocm-ci"
+            echo "NIXL dependency cache missing; will seed: ${nixl_ref}"
+            add_dependency_cache_target "nixl-rocm-ci"
        fi
    fi

@@ -1,10 +1,11 @@
 #!/bin/bash
 set -euox pipefail

-export VLLM_CPU_KVCACHE_SPACE=1 
+export VLLM_CPU_KVCACHE_SPACE=1
 export VLLM_CPU_CI_ENV=1
-# Reduce sub-processes for acceleration
-export TORCH_COMPILE_DISABLE=1 
+# Skip torch.compile via vLLM's --enforce-eager flag (passed below) instead of
+# TORCH_COMPILE_DISABLE=1, which torch 2.12 no longer treats as a silent no-op
+# when callers specify fullgraph=True.
 export VLLM_ENABLE_V1_MULTIPROCESSING=0

 SDE_ARCHIVE="sde-external-10.7.0-2026-02-18-lin.tar.xz"
@@ -49,15 +50,15 @@ wait_for_pid_and_check_log() {
 }

 # Test Sky Lake (AVX512F)
-./sde/sde64 -skl -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_0.log 2>&1 &
+./sde/sde64 -skl -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 --enforce-eager > test_0.log 2>&1 &
 PID_TEST_0=$!

 # Test Cascade Lake (AVX512F + VNNI)
-./sde/sde64 -clx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_1.log 2>&1 &
+./sde/sde64 -clx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 --enforce-eager > test_1.log 2>&1 &
 PID_TEST_1=$!

 # Test Cooper Lake (AVX512F + VNNI + BF16)
-./sde/sde64 -cpx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_2.log 2>&1 &
+./sde/sde64 -cpx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 --enforce-eager > test_2.log 2>&1 &
 PID_TEST_2=$!

 wait_for_pid_and_check_log $PID_TEST_0 test_0.log
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
  key: basic-correctness
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
  key: benchmarks-cli-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -26,7 +26,10 @@ steps:
  - vllm/v1/cudagraph_dispatcher.py
  - vllm/config/compilation.py
  - vllm/compilation
+  - vllm/v1/worker/encoder_cudagraph.py
+  - vllm/v1/worker/encoder_cudagraph_defs.py
  commands:
    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
    - pytest -v -s v1/cudagraph/test_breakable_cudagraph.py
+    - pytest -v -s v1/cudagraph/test_encoder_cudagraph.py
@@ -51,7 +51,7 @@ steps:

 - label: e2e Scheduling (1 GPU)
  key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 35
+  timeout_in_minutes: 53
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/
@@ -39,7 +39,7 @@ steps:
 - label: Entrypoints Integration (API Server)
  key: entrypoints-integration-api-server
  device: h200_35gb
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -59,7 +59,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 1)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -78,7 +78,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 2)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -156,7 +156,7 @@ steps:
 - label: Entrypoints Integration (Pooling)
  device: h200_35gb
  key: entrypoints-integration-pooling
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -31,7 +31,7 @@ steps:

 - label: V1 Sample + Logits
  key: v1-sample-logits
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  device: h200_18gb
  source_file_dependencies:
    - vllm/config/
@@ -90,6 +90,7 @@ steps:
    - tests/v1/kv_offload
    - tests/v1/simple_kv_offload
    - tests/v1/worker
+    - tests/v1/streaming_input
    - tests/v1/kv_connector/unit
    - tests/v1/ec_connector/unit
    - tests/v1/metrics
@@ -103,6 +104,7 @@ steps:
    - pytest -v -s v1/kv_offload
    - pytest -v -s v1/simple_kv_offload
    - pytest -v -s v1/worker
+    - pytest -v -s v1/streaming_input
    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/ec_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/metrics
@@ -143,6 +145,7 @@ steps:
    - pytest -v -s -m 'cpu_test' v1/core
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/test_serial_utils.py
+    - pytest -v -s v1/cudagraph/test_cudagraph_manager.py
    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'cpu_test' v1/metrics

@@ -5,12 +5,14 @@ steps:
 - label: Model Executor
  device: h200_35gb
  key: model-executor
-  timeout_in_minutes: 45
+  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/engine/arg_utils.py
  - vllm/config/model.py
  - vllm/model_executor
+  - vllm/model_executor/warmup
  - tests/model_executor
+  - tests/model_executor/test_jit_warmup.py
  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
  commands:
    - apt-get update && apt-get install -y curl libsodium23
@@ -34,7 +36,9 @@ steps:
      - vllm/engine/arg_utils.py
      - vllm/config/model.py
      - vllm/model_executor
+      - vllm/model_executor/warmup
      - tests/model_executor
+      - tests/model_executor/test_jit_warmup.py
      - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
      - vllm/_aiter_ops.py
      - vllm/platforms/rocm.py
@@ -46,6 +46,19 @@ steps:
      depends_on:
      - image-build-amd

+- label: Inkling Unit Tests (B200)
+  key: inkling-unit-tests-b200
+  timeout_in_minutes: 40
+  device: b200-k8s
+  source_file_dependencies:
+  - vllm/models/inkling/
+  - vllm/cute_utils/
+  - cmake/external_projects/tml_fa4.cmake
+  - tests/models/inkling/
+  commands:
+    # FA4 kernel tests require SM100; the suite skips them elsewhere.
+    - pytest -v -s models/inkling
+
 - label: Basic Models Test (Other CPU) # 5min
  key: basic-models-test-other-cpu
  depends_on:
@@ -137,7 +137,7 @@ steps:

 - label: Language Models Test (MTEB)
  key: language-models-test-mteb
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  optional: true
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
  key: multi-modal-models-standard-1-qwen2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -20,7 +20,7 @@ steps:

 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
  key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -54,7 +54,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
  device: h200_35gb
  key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
@@ -85,7 +85,7 @@ steps:

 - label: Multi-Modal Processor # 44min
  key: multi-modal-processor
-  timeout_in_minutes: 65
+  timeout_in_minutes: 98
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
  device: h200_35gb
  key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 90
+  timeout_in_minutes: 150
  source_file_dependencies:
    - vllm/__init__.py
    - vllm/_aiter_ops.py
@@ -170,3 +170,19 @@ steps:
    - tests/v1/e2e/spec_decode/
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"
+
+- label: Spec Decode DeepSeek MTP Parallel Load (B200)
+  key: spec-decode-deepseek-mtp-parallel-load-b200
+  timeout_in_minutes: 30
+  device: b200-k8s
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/v1/spec_decode/llm_base_proposer.py
+    - vllm/v1/spec_decode/eagle.py
+    - vllm/v1/worker/gpu/spec_decode/eagle/
+    - vllm/model_executor/models/deepseek_mtp.py
+    - vllm/model_executor/models/deepseek_v2.py
+    - tests/v1/e2e/spec_decode/test_mtp_parallel_load.py
+  commands:
+    - pytest -v -s v1/e2e/spec_decode/test_mtp_parallel_load.py
@@ -47,6 +47,7 @@

 # Rust Frontend
 /rust/ @BugenZhao @njhill
+/rust/src/bench @esmeetu
 /build_rust.sh @BugenZhao @njhill
 /rust-toolchain.toml @BugenZhao @njhill
 /.buildkite/test_areas/rust* @BugenZhao @njhill
@@ -181,6 +181,18 @@ pull_request_rules:
      add:
        - performance

+- name: label-quantization
+  description: Automatically apply quantization label
+  conditions:
+    - label != stale
+    - or:
+      - files~=^vllm/model_executor/layers/quantization/
+      - title~=(?i)quant
+  actions:
+    label:
+      add:
+        - quantization
+
 - name: label-qwen
  description: Automatically apply qwen label
  conditions:
@@ -130,6 +130,47 @@ jobs:
                  },
                ],
              },
+              quantization: {
+                keywords: [
+                  {
+                    term: "quantization",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "quantized",
+                    searchIn: "both"
+                  },
+                ],
+              },
+              "intel-gpu": {
+                // Keyword search - matches whole words only (with word boundaries)
+                keywords: [
+                  {
+                    term: "B50",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "B60",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "B70",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "intel gpu",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "Arc GPU",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "BMG",
+                    searchIn: "both"
+                  },
+                ],
+              },
              // Add more label configurations here as needed
              // example: {
              //   keywords: [...],
@@ -491,4 +532,4 @@ jobs:
              issue_number: context.issue.number,
              body: message,
            });
-            core.notice(`Requested missing ROCm info from @${author}: ${missing.map(m => m.name).join(', ')}`);
+            core.notice(`Requested missing ROCm info from @${author}: ${missing.map(m => m.name).join(', ')}`);
@@ -68,8 +68,8 @@ endif()
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.11.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.11.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.13.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.13.0")
 # TORCH_NIGHTLY=1 builds run against unpinned nightly wheels, so the supported-
 # version check would always warn. Only treat it as a nightly build when the
 # value is exactly "1" (the bootstrap exports TORCH_NIGHTLY=0 by default, which
@@ -48,7 +48,7 @@ vLLM is flexible and easy to use with:
 - Tool calling and reasoning parsers
 - OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
 - Efficient multi-LoRA support for dense and MoE layers
- Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
+- Support for NVIDIA GPUs, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.

 vLLM seamlessly supports 200+ model architectures on Hugging Face, including:

@@ -17,7 +17,7 @@ else()
  FetchContent_Declare(
    fmha_sm100
    GIT_REPOSITORY https://github.com/vllm-project/MSA.git
-    GIT_TAG 2e63ec37a0fc29bc20f39cd1a52e0f5affc33a73
+    GIT_TAG 890aaa1a37a598ad17ccff0827fea21540d381fa
    GIT_PROGRESS TRUE
    CONFIGURE_COMMAND ""
    BUILD_COMMAND ""
@@ -22,7 +22,7 @@ if(QUTLASS_SRC_DIR)
  set(qutlass_BINARY_DIR "${CMAKE_BINARY_DIR}/qutlass-binary-dir-unused")
 else()
  set(_QUTLASS_UPSTREAM_REPO "https://github.com/IST-DASLab/qutlass.git")
-  set(_QUTLASS_UPSTREAM_TAG "830d2c4537c7396e14a02a46fbddd18b5d107c65")
+  set(_QUTLASS_UPSTREAM_TAG "e74319e3405ce6d71965732880f5dc1f52371f64")

  set(_qutlass_fc_root "${FETCHCONTENT_BASE_DIR}")
  if(NOT _qutlass_fc_root)
@@ -125,8 +125,6 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
    CUDA_ARCHS "${QUTLASS_ARCHS}"
  )

-  # QuTLASS uses legacy ATen headers and cannot be built with TORCH_TARGET_VERSION.
-  # Keep it as its own extension (registers torch.ops._qutlass_C).
  define_extension_target(
    _qutlass_C
    DESTINATION vllm
@@ -139,9 +137,11 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
    WITH_SOABI)

  target_compile_definitions(_qutlass_C PRIVATE
-    QUTLASS_DISABLE_PYBIND=1
+    QUTLASS_MINIMAL_BUILD=1
    TARGET_CUDA_ARCH=${QUTLASS_TARGET_CC}
-    CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
+    CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1
+    TORCH_TARGET_VERSION=0x020B000000000000ULL
+    USE_CUDA)

  set_property(SOURCE ${QUTLASS_SOURCES} APPEND PROPERTY COMPILE_OPTIONS
    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr --use_fast_math -O3>
@@ -39,7 +39,7 @@ else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG caaa4eb59845388a20b1f435ecaafb4bd9517ad8
+          GIT_TAG ed4b7342bc8f0489dd9b649d5288867e35fc6a32
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
@@ -102,7 +102,9 @@ class TileGemm82 {
    kv_cache_t* __restrict__ curr_b = b_tile;

    for (int32_t k = 0; k < dynamic_k_size; ++k) {
-      auto [fp32_b_0_reg, fp32_b_1_reg] = load_b_pair_vec(curr_b);
+      auto fp32_b_regs = load_b_pair_vec(curr_b);
+      auto fp32_b_0_reg = fp32_b_regs.first;
+      auto fp32_b_1_reg = fp32_b_regs.second;

      float* __restrict__ curr_m_a = curr_a;
      vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
@@ -1025,6 +1025,9 @@ __global__ void gather_and_maybe_dequant_cache(
    batch_offset += offset;
    int32_t block_table_id = batch_offset / block_size;
    int32_t slot_id = batch_offset % block_size;
+    // seq_starts may push the block index past the end of the batch's block
+    // table row.
+    if (block_table_id >= block_table_stride) continue;
    int32_t block_table_offset = batch_id * block_table_stride + block_table_id;
    int32_t block_id = block_table[block_table_offset];
    int64_t cache_offset =
@@ -9,14 +9,16 @@ void topk_softmax(torch::stable::Tensor& topk_weights,
                  torch::stable::Tensor& topk_indices,
                  torch::stable::Tensor& token_expert_indices,
                  torch::stable::Tensor& gating_output, bool renormalize,
-                  std::optional<torch::stable::Tensor> bias);
+                  std::optional<torch::stable::Tensor> bias,
+                  std::optional<torch::stable::Tensor> is_padding);

 void topk_sigmoid(torch::stable::Tensor& topk_weights,
                  torch::stable::Tensor& topk_indices,
                  torch::stable::Tensor& token_expert_indices,
                  torch::stable::Tensor& gating_output, bool renormalize,
                  std::optional<torch::stable::Tensor> bias,
-                  double routed_scaling_factor);
+                  double routed_scaling_factor,
+                  std::optional<torch::stable::Tensor> is_padding);

 void topk_softplus_sqrt(
    torch::stable::Tensor& topk_weights, torch::stable::Tensor& topk_indices,
@@ -25,7 +27,8 @@ void topk_softplus_sqrt(
    double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid);
+    const std::optional<torch::stable::Tensor>& tid2eid,
+    const std::optional<torch::stable::Tensor>& is_padding);

 void moe_sum(torch::stable::Tensor& input, torch::stable::Tensor& output,
             std::optional<torch::stable::Tensor> topk_ids,
@@ -174,7 +174,8 @@ __launch_bounds__(TPB) __global__ void moeTopK(
    const int end_expert,
    const bool renormalize,
    const float* bias,
-    const double routed_scaling_factor)
+    const double routed_scaling_factor,
+    const bool* is_padding)
 {

    using cub_kvp = cub::KeyValuePair<int, float>;
@@ -228,12 +229,14 @@ __launch_bounds__(TPB) __global__ void moeTopK(
            const int expert = result_kvp.key;
            const bool node_uses_expert = expert >= start_expert && expert < end_expert;
            const bool should_process_row = row_is_active && node_uses_expert;
+            const bool is_pad_row = is_padding != nullptr && is_padding[block_row];

            const int idx = k * block_row + k_idx;
            // Return the unbiased scores for output weights
            output[idx] = inputs_after_softmax[thread_read_offset + expert];
-            indices[idx] = should_process_row ? (expert - start_expert) : num_experts;
-            assert(indices[idx] >= 0);
+            indices[idx] = is_pad_row ? static_cast<IndType>(-1)
+                                       : (should_process_row ? (expert - start_expert) : num_experts);
+            assert(is_pad_row || indices[idx] >= 0);
            source_rows[idx] = k_idx * num_rows + block_row;
            if (renormalize) {
                selected_sum += inputs_after_softmax[thread_read_offset + expert];
@@ -277,7 +280,7 @@ template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WA
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
    void topkGating(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-        const float* bias, const double routed_scaling_factor)
+        const float* bias, const double routed_scaling_factor, const bool* is_padding)
 {
    static_assert(std::is_same_v<InputType, float> || std::is_same_v<InputType, __nv_bfloat16> ||
                      std::is_same_v<InputType, __half>,
@@ -545,12 +548,14 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
            // Add a guard to ignore experts not included by this node
            const bool node_uses_expert = expert >= start_expert && expert < end_expert;
            const bool should_process_row = row_is_active && node_uses_expert;
+            const bool is_pad_row = is_padding != nullptr && is_padding[thread_row];

            // The lead thread from each sub-group will write out the final results to global memory. (This will be a
            // single) thread per row of the input/output matrices.
            const int idx = k * thread_row + k_idx;
            output[idx] = max_val;
-            indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
+            indices[idx] = is_pad_row ? static_cast<IndType>(-1)
+                                       : (should_process_row ? (expert - start_expert) : NUM_EXPERTS);
            source_rows[idx] = k_idx * num_rows + thread_row;
            if (renormalize) {
                selected_sum += max_val;
@@ -605,7 +610,7 @@ struct TopkConstants
 template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType, ScoringFunc SF>
 void topkGatingLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
    int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-    const float* bias, const double routed_scaling_factor, cudaStream_t stream)
+    const float* bias, const double routed_scaling_factor, cudaStream_t stream, const bool* is_padding)
 {
    static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
@@ -616,7 +621,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa

    dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
    topkGating<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM, IndType, InputType, SF><<<num_blocks, block_dim, 0, stream>>>(
-        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize, bias, routed_scaling_factor);
+        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize, bias, routed_scaling_factor, is_padding);
 }

 #ifndef USE_ROCM
@@ -627,7 +632,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                             IndType, InputType, SF>(                         \
        gating_output, nullptr, topk_weights, topk_indices,                   \
        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,  \
-        bias, routed_scaling_factor, stream);
+        bias, routed_scaling_factor, stream, is_padding);
 #else
  #define LAUNCH_TOPK(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                    \
    if (WARP_SIZE == 64) {                                                     \
@@ -635,13 +640,13 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, routed_scaling_factor, stream, is_padding);                                            \
    } else if (WARP_SIZE == 32) {                                              \
      topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES,       \
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, routed_scaling_factor, stream, is_padding);                                            \
    } else {                                                                   \
      assert(false &&                                                          \
             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
@@ -661,7 +666,8 @@ void topkGatingKernelLauncher(
    const bool renormalize,
    const float* bias,
    const double routed_scaling_factor,
-    cudaStream_t stream) {
+    cudaStream_t stream,
+    const bool* is_padding) {
    static constexpr int WARPS_PER_TB = 4;
    static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
 #ifndef USE_ROCM
@@ -736,7 +742,7 @@ void topkGatingKernelLauncher(
            }
            moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
                workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
-                num_experts, topk, 0, num_experts, renormalize, bias, routed_scaling_factor);
+                num_experts, topk, 0, num_experts, renormalize, bias, routed_scaling_factor, is_padding);
        }
    }
 }
@@ -755,7 +761,8 @@ void dispatch_topk_launch(
    int num_tokens, int num_experts, int topk, bool renormalize,
    std::optional<torch::stable::Tensor> bias,
    double routed_scaling_factor,
-    cudaStream_t stream)
+    cudaStream_t stream,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const float* bias_ptr = nullptr;
    if (bias.has_value()) {
@@ -769,6 +776,18 @@ void dispatch_topk_launch(
      bias_ptr = bias_tensor.const_data_ptr<float>();
    }

+    const bool* is_padding_ptr = nullptr;
+    if (is_padding.has_value()) {
+      const torch::stable::Tensor& is_padding_tensor = is_padding.value();
+      STD_TORCH_CHECK(is_padding_tensor.scalar_type() == torch::headeronly::ScalarType::Bool,
+                      "is_padding tensor must be bool");
+      STD_TORCH_CHECK(is_padding_tensor.dim() == 1, "is_padding tensor must be 1D");
+      STD_TORCH_CHECK(is_padding_tensor.size(0) == num_tokens,
+                      "is_padding size mismatch, expected: ", num_tokens);
+      STD_TORCH_CHECK(is_padding_tensor.is_contiguous(), "is_padding tensor must be contiguous");
+      is_padding_ptr = is_padding_tensor.const_data_ptr<bool>();
+    }
+
    if (topk_indices.scalar_type() == torch::headeronly::ScalarType::Int) {
        vllm::moe::topkGatingKernelLauncher<int, ComputeType, SF>(
            reinterpret_cast<const ComputeType*>(gating_output.const_data_ptr()),
@@ -777,7 +796,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    } else if (topk_indices.scalar_type() == torch::headeronly::ScalarType::UInt32) {
        vllm::moe::topkGatingKernelLauncher<uint32_t, ComputeType, SF>(
            reinterpret_cast<const ComputeType*>(gating_output.const_data_ptr()),
@@ -786,7 +805,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    } else {
        STD_TORCH_CHECK(topk_indices.scalar_type() == torch::headeronly::ScalarType::Long);
        vllm::moe::topkGatingKernelLauncher<int64_t, ComputeType, SF>(
@@ -796,7 +815,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    }
 }

@@ -806,7 +825,8 @@ void topk_softmax(
    torch::stable::Tensor& token_expert_indices,        // [num_tokens, topk]
    torch::stable::Tensor& gating_output,               // [num_tokens, num_experts]
    bool renormalize,
-    std::optional<torch::stable::Tensor> bias)
+    std::optional<torch::stable::Tensor> bias,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const int num_experts = gating_output.size(-1);
    const auto num_tokens = gating_output.numel() / num_experts;
@@ -825,15 +845,15 @@ void topk_softmax(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -846,7 +866,8 @@ void topk_sigmoid(
    torch::stable::Tensor& gating_output,               // [num_tokens, num_experts]
    bool renormalize,
    std::optional<torch::stable::Tensor> bias,
-    double routed_scaling_factor)
+    double routed_scaling_factor,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const int num_experts = gating_output.size(-1);
    const auto num_tokens = gating_output.numel() / num_experts;
@@ -865,15 +886,15 @@ void topk_sigmoid(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -80,22 +80,27 @@ __launch_bounds__(128) __global__
                                  OutIndType* indices, int num_rows,
                                  int num_experts, float routed_scaling_factor,
                                  const HashIndType* input_ids,
-                                  const HashIndType* tid2eid) {
+                                  const HashIndType* tid2eid,
+                                  const bool* is_padding) {
  const int warp = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
  const int lane = threadIdx.x % 32;
  if (warp >= num_rows) return;
  const int64_t token_id = load_index_as_int64(input_ids, warp);
+  const bool is_pad_row = is_padding != nullptr && is_padding[warp];

  #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
  cudaGridDependencySynchronize();
  #endif
  int expert = 0;
  float weight = 0.f;
-  if (lane < 6) {
+  if (lane < 6 && !is_pad_row) {
    // only load and calculate for 6 experts
    expert = static_cast<int>(tid2eid[token_id * 6 + lane]);
    const float x = input[warp * num_experts + expert];
    weight = sqrtf(fmaxf(x, 0.f) + __logf(1.f + __expf(-fabsf(x))));
+    if (isnan(weight)) {
+      weight = 0.f;
+    }
  }
  float weight_sum = weight;
  #pragma unroll
@@ -111,7 +116,8 @@ __launch_bounds__(128) __global__
    const int offset = warp * 6 + lane;
    output[offset] =
        weight * routed_scaling_factor / (weight_sum > 0.f ? weight_sum : 1.f);
-    indices[offset] = static_cast<OutIndType>(expert);
+    indices[offset] = !is_pad_row ? static_cast<OutIndType>(expert)
+                                  : static_cast<OutIndType>(-1);
  }
 }

@@ -120,7 +126,8 @@ void launchDsv4HashTopk(const float* input, float* output, OutIndType* indices,
                        int num_rows, int num_experts,
                        double routed_scaling_factor,
                        const HashIndType* input_ids,
-                        const HashIndType* tid2eid, cudaStream_t stream) {
+                        const HashIndType* tid2eid, cudaStream_t stream,
+                        const bool* is_padding) {
  if (num_rows == 0) return;
  auto* kernel = &dsv4HashTopkSoftplusSqrt<OutIndType, HashIndType>;
  cudaLaunchConfig_t config = {};
@@ -134,7 +141,7 @@ void launchDsv4HashTopk(const float* input, float* output, OutIndType* indices,
  config.numAttrs = 1;
  const float scale = static_cast<float>(routed_scaling_factor);
  cudaLaunchKernelEx(&config, kernel, input, output, indices, num_rows,
-                     num_experts, scale, input_ids, tid2eid);
+                     num_experts, scale, input_ids, tid2eid, is_padding);
 }
 #endif

@@ -166,7 +173,8 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
        const int num_rows, IndType* indices, int* source_rows, const int k,
        const int start_expert, const int end_expert, const bool renormalize,
        double routed_scaling_factor, const float* correction_bias,
-        const HashIndType* input_ids, const HashIndType* tid2eid) {
+        const HashIndType* input_ids, const HashIndType* tid2eid,
+        const bool* is_padding) {
  static_assert(std::is_same_v<InputType, float> ||
                    std::is_same_v<InputType, __nv_bfloat16> ||
                    std::is_same_v<InputType, __half>,
@@ -231,6 +239,7 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
    return;
  }
  const bool row_is_active = finished ? !finished[thread_row] : true;
+  const bool is_pad_row = is_padding != nullptr && is_padding[thread_row];

  // We finally start setting up the read pointers for each thread. First, each
  // thread jumps to the start of the row it will read.
@@ -249,9 +258,12 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
  cudaGridDependencySynchronize();
 #endif

-  // NOTE(zhuhaoran): dispatch different input types loading, BF16/FP16 convert
-  // to float
-  if constexpr (std::is_same_v<InputType, float>) {
+  if (is_pad_row) {
+#pragma unroll
+    for (int ii = 0; ii < VPT; ++ii) {
+      row_chunk[ii] = 0.f;
+    }
+  } else if constexpr (std::is_same_v<InputType, float>) {
    using VecType = AlignedArray<float, ELTS_PER_LDG>;
    VecType* row_chunk_vec_ptr = reinterpret_cast<VecType*>(&row_chunk);
    const VecType* vec_thread_read_ptr =
@@ -315,12 +327,22 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
  if constexpr (USE_HASH) {
    const int64_t token_id = load_index_as_int64(input_ids, thread_row);
    const int64_t token_expert_offset = token_id * static_cast<int64_t>(k);
+    if (!is_pad_row) {
 #pragma unroll
-    for (int ii = 0; ii < VPT; ++ii) {
-      float val = row_chunk[ii];
-      float val_b = val * beta;
-      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-      row_chunk[ii] = sqrtf(val);
+      for (int ii = 0; ii < VPT; ++ii) {
+        float val = row_chunk[ii];
+        float val_b = val * beta;
+        val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+        val = sqrtf(val);
+
+        // Dummy/padding tokens can result in NaN values, so
+        // clamp them to 0.0. Note: this clamp could likely be removed if
+        // 'is_padding' is made mandatory
+        if (isnan(val)) {
+          val = 0.f;
+        }
+        row_chunk[ii] = val;
+      }
    }
    float selected_sum = 0.f;
 #pragma unroll
@@ -335,7 +357,8 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
                               local_id;
        if (expert == expert_idx) {
-          indices[idx] = static_cast<IndType>(expert);
+          indices[idx] = !is_pad_row ? static_cast<IndType>(expert)
+                                     : static_cast<IndType>(-1);
          selected_sum += row_chunk[ii];
          break;
        }
@@ -379,23 +402,31 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
 #endif
    return;
  } else {
+    if (!is_pad_row) {
 #pragma unroll
-    for (int ii = 0; ii < VPT; ++ii) {
-      float val = row_chunk[ii];
-      float val_b = val * beta;
-      // Compute softplus: log(1 + exp(val)) with numerical stability
-      // When val > threshold, softplus(x) ≈ x to avoid exp overflow
-      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-      val = sqrtf(val);
-      if (correction_bias) {
-        const int group_id = ii / ELTS_PER_LDG;
-        const int local_id = ii % ELTS_PER_LDG;
-        const int expert_idx = first_elt_read_by_thread +
-                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
-                               local_id;
-        val = val + correction_bias[expert_idx];
+      for (int ii = 0; ii < VPT; ++ii) {
+        float val = row_chunk[ii];
+        float val_b = val * beta;
+        // Compute softplus: log(1 + exp(val)) with numerical stability
+        // When val > threshold, softplus(x) ≈ x to avoid exp overflow
+        val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+        val = sqrtf(val);
+        // Dummy/padding tokens can result in NaN values, so
+        // clamp them to 0.0. Note: this clamp could likely be removed if
+        // 'is_padding' is made mandatory
+        if (isnan(val)) {
+          val = 0.f;
+        }
+        if (correction_bias) {
+          const int group_id = ii / ELTS_PER_LDG;
+          const int local_id = ii % ELTS_PER_LDG;
+          const int expert_idx = first_elt_read_by_thread +
+                                 group_id * THREADS_PER_ROW * ELTS_PER_LDG +
+                                 local_id;
+          val = val + correction_bias[expert_idx];
+        }
+        row_chunk[ii] = val;
      }
-      row_chunk[ii] = val;
    }

    // Original TopK path: find top-k experts by score
@@ -450,18 +481,19 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
        // Add a guard to ignore experts not included by this node
        const bool node_uses_expert =
            expert >= start_expert && expert < end_expert;
-        const bool should_process_row = row_is_active && node_uses_expert;
+        const bool should_process_row =
+            row_is_active && node_uses_expert && !is_pad_row;

        // The lead thread from each sub-group will write out the final results
        // to global memory. (This will be a single) thread per row of the
        // input/output matrices.
        const int idx = k * thread_row + k_idx;
-        if (correction_bias != nullptr) {
+        if (correction_bias != nullptr && should_process_row) {
          max_val -= correction_bias[expert];
        }
        output[idx] = max_val;
        indices[idx] =
-            should_process_row ? (expert - start_expert) : NUM_EXPERTS;
+            !is_pad_row ? expert - start_expert : static_cast<IndType>(-1);
        source_rows[idx] = k_idx * num_rows + thread_row;
        if (renormalize) {
          selected_sum += max_val;
@@ -544,7 +576,7 @@ void topkGatingSoftplusSqrtLauncherHelper(
    const int start_expert, const int end_expert, const bool renormalize,
    double routed_scaling_factor, const float* correction_bias,
    const bool use_hash, const HashIndType* input_ids,
-    const HashIndType* tid2eid, cudaStream_t stream) {
+    const HashIndType* tid2eid, cudaStream_t stream, const bool* is_padding) {
  static constexpr int BYTES_PER_LDG =
      MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
  using Constants =
@@ -573,12 +605,12 @@ void topkGatingSoftplusSqrtLauncherHelper(
    cudaLaunchKernelEx(&config, kernel, input, finished, output, num_rows,
                       indices, source_row, k, start_expert, end_expert,
                       renormalize, routed_scaling_factor, correction_bias,
-                       input_ids, tid2eid);
+                       input_ids, tid2eid, is_padding);
 #else
    kernel<<<num_blocks, block_dim, 0, stream>>>(
        input, finished, output, num_rows, indices, source_row, k, start_expert,
        end_expert, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid);
+        input_ids, tid2eid, is_padding);
 #endif
  })
 }
@@ -592,7 +624,7 @@ void topkGatingSoftplusSqrtLauncherHelper(
        gating_output, nullptr, topk_weights, topk_indices,                    \
        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,   \
        routed_scaling_factor, correction_bias, use_hash, input_ids, tid2eid,  \
-        stream);
+        stream, is_padding);
 #else
  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
    if (WARP_SIZE == 64) {                                                     \
@@ -601,14 +633,14 @@ void topkGatingSoftplusSqrtLauncherHelper(
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
+          tid2eid, stream, is_padding);                                        \
    } else if (WARP_SIZE == 32) {                                              \
      topkGatingSoftplusSqrtLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32,      \
                                           MAX_BYTES>(                         \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
+          tid2eid, stream, is_padding);                                        \
    } else {                                                                   \
      assert(false &&                                                          \
             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
@@ -622,14 +654,14 @@ void topkGatingSoftplusSqrtKernelLauncher(
    const int topk, const bool renormalize, double routed_scaling_factor,
    const float* correction_bias, const bool use_hash,
    const HashIndType* input_ids, const HashIndType* tid2eid,
-    cudaStream_t stream) {
+    cudaStream_t stream, const bool* is_padding) {
 #ifndef USE_ROCM
  if constexpr (std::is_same_v<InputType, float>) {
    if (use_hash && topk == 6 && renormalize &&
        (num_experts == 256 || num_experts == 384)) {
      launchDsv4HashTopk<IndType, HashIndType>(
          gating_output, topk_weights, topk_indices, num_tokens, num_experts,
-          routed_scaling_factor, input_ids, tid2eid, stream);
+          routed_scaling_factor, input_ids, tid2eid, stream, is_padding);
      return;
    }
  }
@@ -728,7 +760,8 @@ void dispatch_topk_softplus_sqrt_launch(
    int num_experts, int topk, bool renormalize, double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid, cudaStream_t stream) {
+    const std::optional<torch::stable::Tensor>& tid2eid, cudaStream_t stream,
+    const std::optional<torch::stable::Tensor>& is_padding) {
  const float* bias_ptr = nullptr;
  if (correction_bias.has_value()) {
    bias_ptr = correction_bias.value().const_data_ptr<float>();
@@ -737,6 +770,22 @@ void dispatch_topk_softplus_sqrt_launch(
  auto launch = [&](auto* topk_indices_ptr) {
    using OutIndType =
        typename std::remove_pointer<decltype(topk_indices_ptr)>::type;
+
+    const bool* is_padding_ptr = nullptr;
+    if (is_padding.has_value()) {
+      const torch::stable::Tensor& is_padding_tensor = is_padding.value();
+      STD_TORCH_CHECK(is_padding_tensor.scalar_type() ==
+                          torch::headeronly::ScalarType::Bool,
+                      "is_padding tensor must be bool");
+      STD_TORCH_CHECK(is_padding_tensor.dim() == 1,
+                      "is_padding tensor must be 1D");
+      STD_TORCH_CHECK(is_padding_tensor.size(0) == num_tokens,
+                      "is_padding size mismatch, expected: ", num_tokens);
+      STD_TORCH_CHECK(is_padding_tensor.is_contiguous(),
+                      "is_padding tensor must be contiguous");
+      is_padding_ptr = is_padding_tensor.const_data_ptr<bool>();
+    }
+
    if (tid2eid.has_value()) {
      STD_TORCH_CHECK(input_ids.has_value(),
                      "input_ids is required for hash MoE");
@@ -751,7 +800,7 @@ void dispatch_topk_softplus_sqrt_launch(
            topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
            num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
            bias_ptr, true, input_ids.value().const_data_ptr<int64_t>(),
-            tid2eid.value().const_data_ptr<int64_t>(), stream);
+            tid2eid.value().const_data_ptr<int64_t>(), stream, is_padding_ptr);
      } else {
        STD_TORCH_CHECK(tid2eid.value().scalar_type() ==
                        torch::headeronly::ScalarType::Int);
@@ -761,7 +810,7 @@ void dispatch_topk_softplus_sqrt_launch(
            topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
            num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
            bias_ptr, true, input_ids.value().const_data_ptr<int>(),
-            tid2eid.value().const_data_ptr<int>(), stream);
+            tid2eid.value().const_data_ptr<int>(), stream, is_padding_ptr);
      }
    } else {
      vllm::moe::topkGatingSoftplusSqrtKernelLauncher<OutIndType, ComputeType>(
@@ -769,7 +818,7 @@ void dispatch_topk_softplus_sqrt_launch(
          topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
          num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
          bias_ptr, false, static_cast<const OutIndType*>(nullptr),
-          static_cast<const OutIndType*>(nullptr), stream);
+          static_cast<const OutIndType*>(nullptr), stream, is_padding_ptr);
    }
  };

@@ -793,7 +842,8 @@ void topk_softplus_sqrt(
    bool renormalize, double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid) {
+    const std::optional<torch::stable::Tensor>& tid2eid,
+    const std::optional<torch::stable::Tensor>& is_padding) {
  const int num_experts = gating_output.size(-1);
  const auto num_tokens = gating_output.numel() / num_experts;
  const int topk = topk_weights.size(-1);
@@ -806,21 +856,22 @@ void topk_softplus_sqrt(
    dispatch_topk_softplus_sqrt_launch<float>(
        gating_output.const_data_ptr<float>(), topk_weights, topk_indices,
        token_expert_indices, num_tokens, num_experts, topk, renormalize,
-        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream);
+        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream,
+        is_padding);
  } else if (gating_output.scalar_type() ==
             torch::headeronly::ScalarType::Half) {
    dispatch_topk_softplus_sqrt_launch<__half>(
        reinterpret_cast<const __half*>(gating_output.const_data_ptr()),
        topk_weights, topk_indices, token_expert_indices, num_tokens,
        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
+        input_ids, tid2eid, stream, is_padding);
  } else if (gating_output.scalar_type() ==
             torch::headeronly::ScalarType::BFloat16) {
    dispatch_topk_softplus_sqrt_launch<__nv_bfloat16>(
        reinterpret_cast<const __nv_bfloat16*>(gating_output.const_data_ptr()),
        topk_weights, topk_indices, token_expert_indices, num_tokens,
        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
+        input_ids, tid2eid, stream, is_padding);
  } else {
    STD_TORCH_CHECK(false, "Unsupported gating_output data type: ",
                    gating_output.scalar_type());
@@ -8,19 +8,19 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_moe_C, m) {
  m.def(
      "topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, Tensor? "
-      "bias) -> ()");
+      "bias, Tensor? is_padding) -> ()");

  // Apply topk sigmoid to the gating outputs.
  m.def(
      "topk_sigmoid(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, "
-      "Tensor? bias, float routed_scaling_factor) -> ()");
+      "Tensor? bias, float routed_scaling_factor, Tensor? is_padding) -> ()");

  m.def(
      "topk_softplus_sqrt(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, float "
      "routed_scaling_factor, Tensor? "
-      "bias, Tensor? input_ids, Tensor? tid2eid) -> ()");
+      "bias, Tensor? input_ids, Tensor? tid2eid, Tensor? is_padding) -> ()");

  // Calculate the result of moe by summing up the partial results
  // from all selected experts. topk_ids/expert_map are optional and, when
@@ -39,11 +39,15 @@ __global__ void marlin_int4_fp8_preprocess_kernel_awq(
    // AWQ zeros: (size_k // group_size, size_n // 8)
    const int32_t* __restrict__ qzeros, int32_t size_n, int32_t size_k,
    int32_t group_size) {
-  int32_t val =
-      qweight[(blockIdx.x * 32 + threadIdx.x) * size_n / 8 + blockIdx.y];
-  int32_t zero =
-      qzeros[(blockIdx.x * 32 + threadIdx.x) / group_size * size_n / 8 +
-             blockIdx.y];
+  // Thread mapping: threadIdx.x -> column dim (coalesced read within a row),
+  // blockIdx.x -> row dim. Adjacent threads read consecutive int32 in the
+  // same row (stride 1) instead of striding across rows (stride size_n/8).
+  int col = blockIdx.y * 32 + threadIdx.x;
+  if (col >= size_n / 8) return;
+  (void)size_k;
+
+  int32_t val = qweight[blockIdx.x * (size_n / 8) + col];
+  int32_t zero = qzeros[blockIdx.x / group_size * (size_n / 8) + col];
  int32_t new_val = 0;

 #pragma unroll
@@ -58,7 +62,7 @@ __global__ void marlin_int4_fp8_preprocess_kernel_awq(
    zero >>= 4;
  }

-  output[(blockIdx.x * 32 + threadIdx.x) * size_n / 8 + blockIdx.y] = new_val;
+  output[blockIdx.x * (size_n / 8) + col] = new_val;
 }

 torch::stable::Tensor marlin_int4_fp8_preprocess(
@@ -102,7 +106,7 @@ torch::stable::Tensor marlin_int4_fp8_preprocess(
                    "qweight.size(0) % qzeros.size(0) != 0");
    STD_TORCH_CHECK(group_size % 8 == 0, "group_size % 8 != 0");

-    dim3 blocks(size_k / 32, size_n / 8);
+    dim3 blocks(size_k, (size_n / 8 + 31) / 32);
    marlin_int4_fp8_preprocess_kernel_awq<<<blocks, 32, 0, stream>>>(
        reinterpret_cast<const int32_t*>(qweight.const_data_ptr()),
        reinterpret_cast<int32_t*>(output.mutable_data_ptr()),
@@ -22,7 +22,7 @@
 #   docker buildx bake -f docker/docker-bake.hcl -f docker/versions.json
 # =============================================================================

-ARG CUDA_VERSION=13.0.2
+ARG CUDA_VERSION=13.0.3
 ARG PYTHON_VERSION=3.12
 ARG UBUNTU_VERSION=22.04

@@ -793,7 +793,7 @@ RUN --mount=type=cache,target=/opt/uv/cache \
 # Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
 # https://docs.flashinfer.ai/installation.html
 # From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.14
+ARG FLASHINFER_VERSION=0.6.15.post1
 RUN --mount=type=cache,target=/opt/uv/cache \
    uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
        --index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
@@ -339,18 +339,17 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/rust /rust
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/rust-toolchain.toml /rust-toolchain.toml
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1

-# RIXL/UCX build stages
-FROM base AS build_rixl
-ARG RIXL_BRANCH="39be1de8"
-ARG RIXL_REPO="https://github.com/ROCm/RIXL.git"
-ARG UCX_BRANCH="bfb51733"
+# NIXL/UCX build stages
+FROM base AS build_nixl
+ARG NIXL_BRANCH="231d56753047c989062a5cb2ac703a1ad761c7d2"
+ARG NIXL_REPO="https://github.com/ai-dynamo/nixl.git"
+ARG UCX_BRANCH="96e58a16039f6d7d213bc967b8069238742c5194"
 ARG UCX_REPO="https://github.com/openucx/ucx.git"
 ENV ROCM_PATH=/opt/rocm
 ENV UCX_HOME=/usr/local/ucx
-ENV RIXL_HOME=/usr/local/rixl
-ENV RIXL_BENCH_HOME=/usr/local/rixl_bench
+ENV NIXL_HOME=/usr/local/nixl

-# RIXL build system dependences and RDMA support
+# NIXL build system dependencies and RDMA support
 RUN apt-get -y update && apt-get -y install autoconf libtool pkg-config \
    libgrpc-dev \
    libgrpc++-dev \
@@ -368,7 +367,8 @@ RUN apt-get -y update && apt-get -y install autoconf libtool pkg-config \
    && rm -rf /var/lib/apt/lists/*

 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system meson auditwheel patchelf tomlkit
+    uv pip install --system meson meson-python pybind11 pyyaml types-PyYAML \
+        auditwheel build patchelf pytest tomlkit "setuptools>=80.9.0"

 RUN --mount=type=cache,target=/root/.cache/ccache \
    cd /usr/local/src && \
@@ -396,30 +396,50 @@ ENV PATH=/usr/local/ucx/bin:$PATH
 ENV LD_LIBRARY_PATH=${UCX_HOME}/lib:${LD_LIBRARY_PATH}

 RUN --mount=type=cache,target=/root/.cache/ccache \
-    git clone ${RIXL_REPO} /opt/rixl && \
-    cd /opt/rixl && \
-    git checkout ${RIXL_BRANCH} && \
+    git clone ${NIXL_REPO} /opt/nixl && \
+    cd /opt/nixl && \
+    git checkout ${NIXL_BRANCH} && \
    CC="ccache gcc" CXX="ccache g++" \
-    meson setup build --prefix=${RIXL_HOME} \
+    meson setup build --prefix=${NIXL_HOME} \
                     -Ducx_path=${UCX_HOME} \
-                     -Drocm_path=${ROCM_PATH} && \
+                     -Dwheel_variant=rocm \
+                     -Dbuild_tests=false \
+                     -Dbuild_examples=false && \
    cd build && \
    ninja -j$(nproc) && \
-    ninja install
+    ninja install && \
+    echo "${NIXL_HOME}/lib/$(uname -m)-linux-gnu" \
+        > /etc/ld.so.conf.d/nixl.conf && \
+    echo "${NIXL_HOME}/lib/$(uname -m)-linux-gnu/plugins" \
+        >> /etc/ld.so.conf.d/nixl.conf && \
+    ldconfig

-# Generate RIXL wheel
+# Generate the ROCm NIXL wheel. Upstream's generic wheel helper detects CUDA,
+# so configure the ROCm wheel variant directly through Meson.
 # Exclude libcore and libpull from auditwheel: transitive dependencies
 # that are not shipped in the wheel and vary across base images.
-RUN cd /opt/rixl && \
-    sed -i "s/--exclude 'libamdhip64\*'/--exclude 'libamdhip64*' --exclude 'libcore*' --exclude 'libpull*'/" \
-        contrib/build-wheel.sh && \
-    mkdir -p /app/install && \
-    _ucx_install_dir=${UCX_HOME} \
-    ./contrib/build-wheel.sh \
-        --output-dir /app/install \
-        --rocm-dir ${ROCM_PATH} \
+RUN cd /opt/nixl && \
+    ./contrib/tomlutil.py --wheel-name nixl-rocm pyproject.toml && \
+    CC="ccache gcc" CXX="ccache g++" \
+    uv build --wheel --no-build-isolation --out-dir /tmp/nixl_wheels \
+        --python ${PYTHON_VERSION} \
+        -Csetup-args=-Ducx_path=${UCX_HOME} \
+        -Csetup-args=-Dwheel_variant=rocm \
+        -Csetup-args=-Dbuild_tests=false \
+        -Csetup-args=-Dbuild_examples=false && \
+    mkdir -p /tmp/nixl_wheels/repaired /app/install && \
+    auditwheel repair \
+        --exclude 'libamdhip64*' \
+        --exclude 'libcore*' \
+        --exclude 'libpull*' \
+        /tmp/nixl_wheels/nixl_rocm*.whl \
+        --plat manylinux_2_34_$(uname -m) \
+        --wheel-dir /tmp/nixl_wheels/repaired && \
+    ./contrib/wheel_add_ucx_plugins.py \
        --ucx-plugins-dir ${UCX_HOME}/lib/ucx \
-        --nixl-plugins-dir ${RIXL_HOME}/lib/x86_64-linux-gnu/plugins
+        --nixl-plugins-dir ${NIXL_HOME}/lib/$(uname -m)-linux-gnu/plugins \
+        /tmp/nixl_wheels/repaired/*.whl && \
+    cp /tmp/nixl_wheels/repaired/*.whl /app/install

 # ROCShmem build stage - split from DeepEP so changing DEEPEP_BRANCH does not
 # invalidate the slow ROCShmem build.
@@ -660,10 +680,10 @@ RUN if [ "${DEEPEP_NIC}" = "cx7" ] || [ "${DEEPEP_NIC}" = "io" ]; then \
    ninja && ninja install && ldconfig && rm -rf /tmp/rdma-core; \
 fi

-# Install RIXL + DeepEP wheels.
-RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
+# Install NIXL + DeepEP wheels.
+RUN --mount=type=bind,from=build_nixl,src=/app/install,target=/nixl_install \
    --mount=type=bind,from=build_deepep,src=/app/deep_install,target=/deep_install \
-    uv pip install --system /rixl_install/*.whl /deep_install/*.whl
+    uv pip install --system /nixl_install/*.whl /deep_install/*.whl

 # Copy ROCShmem runtime libraries.
 COPY --from=build_rocshmem /opt/rocshmem /opt/rocshmem
@@ -724,6 +744,7 @@ ENV MIOPEN_DEBUG_CONV_GEMM=0
 # Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc.
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+ENV UCX_RMA_PPLN_ENABLE=y

 # ROCm profiler limits workaround.
 RUN echo "ROCTRACER_MAX_EVENTS=10000000" > ${COMMON_WORKDIR}/libkineto.conf
@@ -796,9 +817,9 @@ RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
    && pip uninstall -y vllm \
    && uv pip install --system *.whl

-# Install RIXL wheel
-RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
-    uv pip install --system /rixl_install/*.whl
+# Install NIXL ROCm wheel
+RUN --mount=type=bind,from=build_nixl,src=/app/install,target=/nixl_install \
+    uv pip install --system /nixl_install/*.whl

 ARG COMMON_WORKDIR
 ARG BASE_IMAGE
@@ -813,6 +834,7 @@ COPY --from=export_vllm /docker ${COMMON_WORKDIR}/vllm/docker
 # Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+ENV UCX_RMA_PPLN_ENABLE=y

 ENV TOKENIZERS_PARALLELISM=false

@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.16.post3"
+ARG AITER_BRANCH="v0.1.16.post5"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.1.0"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
@@ -86,6 +86,29 @@ RUN --mount=type=cache,target=/root/.cache/uv \
    mkdir -p /tmp/hf-xet/dist && \
    cp dist/*.whl /tmp/hf-xet/dist/

+# Build LLVM 20 from source for llvmlite (system repos ship LLVM 21 which
+# llvmlite v0.47 does not support; only SystemZ target is needed).
+FROM base AS llvm20-build
+ARG LLVM_VERSION=20.1.8
+WORKDIR /tmp
+RUN microdnf install -y ninja-build gcc gcc-c++ python3 xz && \
+    curl -LO https://github.com/llvm/llvm-project/releases/download/llvmorg-${LLVM_VERSION}/llvm-project-${LLVM_VERSION}.src.tar.xz && \
+    tar -xf llvm-project-${LLVM_VERSION}.src.tar.xz && \
+    cmake -G Ninja -S llvm-project-${LLVM_VERSION}.src/llvm -B build \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_INSTALL_PREFIX=/opt/llvm20 \
+        -DLLVM_TARGETS_TO_BUILD="SystemZ" \
+        -DLLVM_ENABLE_RTTI=ON \
+        -DLLVM_BUILD_TOOLS=OFF \
+        -DLLVM_BUILD_UTILS=ON \
+        -DLLVM_BUILD_EXAMPLES=OFF \
+        -DLLVM_BUILD_TESTS=OFF \
+        -DLLVM_INCLUDE_TESTS=OFF \
+        -DLLVM_INCLUDE_EXAMPLES=OFF \
+        -DLLVM_INCLUDE_BENCHMARKS=OFF && \
+    ninja -C build install && \
+    rm -rf build llvm-project-${LLVM_VERSION}.src*
+
 # Build numba
 FROM python-install AS numba-builder

@@ -96,11 +119,13 @@ WORKDIR /tmp

 # Clone all required dependencies
 RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,from=llvm20-build,source=/opt/llvm20,target=/opt/llvm20 \
    microdnf install ninja-build gcc gcc-c++ -y && \
    git clone --recursive https://github.com/numba/llvmlite.git -b v0.47.0 && \
    git clone --recursive https://github.com/numba/numba.git -b ${NUMBA_VERSION} && \
    cd llvmlite && \
    uv pip install 'cmake<4' 'setuptools<70' numpy && \
+    CMAKE_PREFIX_PATH=/opt/llvm20 LLVM_CONFIG=/opt/llvm20/bin/llvm-config \
    python setup.py bdist_wheel && \
    cd ../numba && \
    if ! grep '#include "dynamic_annotations.h"' numba/_dispatcher.cpp; then \
@@ -158,7 +183,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     NUMBA_WHL_FILE=$(ls /tmp/numba-wheels/*.whl) && \
     OPENCV_WHL_FILE=$(ls /tmp/opencv-wheels/*.whl) && \
     uv pip install -v \
-        $ARROW_WHL_FILE \
        $VISION_WHL_FILE \
        $HF_XET_WHL_FILE \
        $LLVM_WHL_FILE \
@@ -59,7 +59,7 @@ variable "PYTORCH_ROCM_ARCH" {
 }

 # Pre-built CI base image (Tier 1). Per-PR builds pull this instead of
-# rebuilding RIXL/DeepEP/torchcodec from scratch. The ci_base stage in
+# rebuilding NIXL/DeepEP/torchcodec from scratch. The ci_base stage in
 # Dockerfile.rocm inherits from base, so CI_BASE_IMAGE only affects the test
 # stage and is irrelevant when building --target ci_base itself.
 variable "CI_BASE_IMAGE" {
@@ -75,7 +75,7 @@ variable "CI_MAX_JOBS" {
 # Upstream dependency commit pins -- extracted from Dockerfile.rocm by
 # ci-bake-rocm.sh at build time. Empty defaults are safe: the cache
 # functions produce no entries when the variable is empty.
-variable "RIXL_BRANCH" {
+variable "NIXL_BRANCH" {
  default = ""
 }

@@ -91,7 +91,7 @@ variable "DEEPEP_BRANCH" {
  default = ""
 }

-variable "RIXL_CACHE_KEY" {
+variable "NIXL_CACHE_KEY" {
  default = ""
 }

@@ -236,7 +236,7 @@ function "get_cache_to_rocm_rust" {
  ])
 }

-# Cache functions for upstream dependency stages (RIXL/UCX, ROCShmem, DeepEP).
+# Cache functions for upstream dependency stages (NIXL/UCX, ROCShmem, DeepEP).
 # These stages are pinned to specific upstream commit hashes, so cache keys use
 # those hashes rather than the Buildkite commit. This means the cache persists
 # across all vLLM commits as long as the upstream dependency pins don't change.
@@ -244,16 +244,16 @@ function "get_cache_to_rocm_rust" {
 function "get_cache_from_rocm_deps" {
  params = []
  result = compact([
-    RIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_CACHE_KEY}" : (RIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH}" : ""),
+    NIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_CACHE_KEY}" : (NIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH}" : ""),
    ROCSHMEM_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rocshmem-rocm-${ROCSHMEM_CACHE_KEY}" : (ROCSHMEM_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rocshmem-rocm-${ROCSHMEM_BRANCH}" : ""),
    DEEPEP_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:deepep-rocm-${DEEPEP_CACHE_KEY}" : (DEEPEP_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:deepep-rocm-${DEEPEP_BRANCH}-rocshmem-${ROCSHMEM_BRANCH}" : ""),
  ])
 }

-function "get_cache_to_rocm_rixl" {
+function "get_cache_to_rocm_nixl" {
  params = []
  result = compact([
-    RIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_CACHE_KEY},mode=min" : (RIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH},mode=min" : ""),
+    NIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_CACHE_KEY},mode=min" : (NIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH},mode=min" : ""),
  ])
 }

@@ -372,11 +372,11 @@ variable "CI_BASE_IMAGE_TAG_STABLE" {
 # in the registry cache keyed by its upstream commit hash. When ci_base rebuilds
 # (e.g., requirements change), these stages are cache hits if their upstream
 # pins haven't changed -- saving ~35min of compilation.
-target "rixl-rocm-ci" {
+target "nixl-rocm-ci" {
  inherits   = ["_common-rocm", "_ci-rocm"]
-  target     = "build_rixl"
+  target     = "build_nixl"
  cache-from = get_cache_from_rocm_deps()
-  cache-to   = get_cache_to_rocm_rixl()
+  cache-to   = get_cache_to_rocm_nixl()
  output     = ["type=cacheonly"]
 }

@@ -396,7 +396,7 @@ target "deepep-rocm-ci" {
  output     = ["type=cacheonly"]
 }

-# Builds only the ci_base stage (RIXL, DeepEP, torchcodec, etc.)
+# Builds only the ci_base stage (NIXL, DeepEP, torchcodec, etc.)
 # Invoked by the ensure-ci-base step when the content hash of ci_base-affecting
 # files drifts from the remote image label. Per-PR builds then pull the result
 # as CI_BASE_IMAGE instead of rebuilding those slow layers on every commit.
@@ -412,7 +412,7 @@ target "ci-base-rocm-ci" {
      CI_BASE_IMAGE_TAG_CONTENT_EXTRA != "" ? "type=registry,ref=${CI_BASE_IMAGE_TAG_CONTENT_EXTRA}" : "",
      CI_BASE_IMAGE_TAG_STABLE != "" ? "type=registry,ref=${CI_BASE_IMAGE_TAG_STABLE}" : "",
    ]),
-    # Import upstream dependency caches so RIXL/ROCShmem/DeepEP stages
+    # Import upstream dependency caches so NIXL/ROCShmem/DeepEP stages
    # are cache hits even when ci_base itself needs rebuilding.
    get_cache_from_rocm_deps(),
  )
@@ -424,5 +424,5 @@ target "ci-base-rocm-ci" {
 # Group for ci_base builds -- exports dependency stage caches alongside the
 # ci_base image so future rebuilds can reuse them independently.
 group "ci-base-rocm-ci-with-deps" {
-  targets = ["rixl-rocm-ci", "rocshmem-rocm-ci", "deepep-rocm-ci", "ci-base-rocm-ci"]
+  targets = ["nixl-rocm-ci", "rocshmem-rocm-ci", "deepep-rocm-ci", "ci-base-rocm-ci"]
 }
@@ -53,7 +53,7 @@ variable "CI_BASE_IMAGE" {
 # Upstream dependency commit pins. Plain local bake builds use the Dockerfile
 # ARG defaults. ci-bake-rocm.sh resolves those defaults (plus any env
 # overrides) and writes a small HCL override before invoking CI targets.
-variable "RIXL_BRANCH" {
+variable "NIXL_BRANCH" {
  default = ""
 }

@@ -106,7 +106,7 @@ target "test-rocm" {
  output   = ["type=docker"]
 }

-# CI base image target - builds only the ci_base stage (RIXL, DeepEP,
+# CI base image target - builds only the ci_base stage (NIXL, DeepEP,
 # torchcodec, requirements, etc.). Used by the weekly scheduled build and
 # the auto-rebuild trigger when requirements change in a PR.
 target "ci-base-rocm" {
@@ -2,7 +2,7 @@
  "_comment": "Auto-generated from Dockerfile ARGs. Do not edit manually. Run: python tools/generate_versions_json.py",
  "variable": {
    "CUDA_VERSION": {
-      "default": "13.0.2"
+      "default": "13.0.3"
    },
    "PYTHON_VERSION": {
      "default": "3.12"
@@ -11,10 +11,10 @@
      "default": "22.04"
    },
    "BUILD_BASE_IMAGE": {
-      "default": "nvidia/cuda:13.0.2-devel-ubuntu22.04"
+      "default": "nvidia/cuda:13.0.3-devel-ubuntu22.04"
    },
    "FINAL_BASE_IMAGE": {
-      "default": "nvidia/cuda:13.0.2-base-ubuntu22.04"
+      "default": "nvidia/cuda:13.0.3-base-ubuntu22.04"
    },
    "BUILD_OS": {
      "default": "ubuntu"
@@ -68,7 +68,7 @@
      "default": "true"
    },
    "FLASHINFER_VERSION": {
-      "default": "0.6.14"
+      "default": "0.6.15.post1"
    },
    "GDRCOPY_CUDA_VERSION": {
      "default": "12.8"
@@ -164,8 +164,8 @@ Priority is **1 = highest** (tried first).
 | `FLASHINFER` | XQA† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ❌ | ❌ | ✅ | Decoder | 9.0 |
 | `FLASHINFER` | trtllm-gen† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ✅ | ❌ | ✅ | Decoder | 10.x |
 | `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
-| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
-| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
+| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
+| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
 | `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
 | `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder Only | Any |
 | `HPC_ATTN` | | fp16, bf16 | `auto`, `bfloat16`, `fp8_e4m3` | 64 | 128 | ❌ | ❌ | ❌ | ❌ | Decoder | ≥9.0 |
@@ -75,7 +75,7 @@ vllm serve <model> \
 | `max_tracker_size` | no | `64000` | single-tier | Max entries in the lookup tracker. |
 | `secondary_tiers` | no | `[]` | multi-tier | List of secondary tier configs (see below). |
 | `offload_prompt_only` | no | `true` | both | If `true`, only prompt (prefill) blocks are offloaded; decode blocks are skipped. |
-| `self_describing_kv_events` | no | `false` | single-tier | Opt-in. When `true` *and* KV cache events are enabled (`--kv-events-config` with `enable_kv_cache_events`), the connector emits self-describing block-granular `BlockStored`/`BlockRemoved` payloads (constituent block hashes, whole-chunk `token_ids`, per-block `block_size`, parent hash, LoRA + group/cache-spec metadata) instead of the placeholder fallback, so external KV-event consumers can index offloaded blocks. Inert unless events are enabled. Currently rejected by `TieringOffloadingSpec`. Full-attention groups only; sliding-window/SSM groups keep the placeholder fallback. In chunk mode (`block_size` > GPU block size, or `blocks_per_chunk` > 1), overlapping chunks re-announce shared per-block hashes, so consumers must reference-count (deduplicate) repeated store/remove announcements. |
+| `self_describing_kv_events` | no | `false` | both | Opt-in. When `true` *and* KV cache events are enabled (`--kv-events-config` with `enable_kv_cache_events`), the connector emits self-describing block-granular `BlockStored`/`BlockRemoved` payloads (constituent block hashes, whole-chunk `token_ids`, per-block `block_size`, parent hash, LoRA + group/cache-spec metadata) instead of the placeholder fallback, so external KV-event consumers can index offloaded blocks. Inert unless events are enabled. With `TieringOffloadingSpec`, a CPU promotion is self-describing when a local request observes its primary-tier `HIT` before event translation; otherwise its stored event may retain the placeholder, while a later `HIT` can backfill metadata for removal. Pending-removal/re-promotion races and externally initiated promotions may also produce placeholders, and consumers must ignore removals for unknown hashes. Full-attention groups only; sliding-window/SSM groups keep the placeholder fallback. In chunk mode (`block_size` > GPU block size, or `blocks_per_chunk` > 1), overlapping chunks re-announce shared per-block hashes, so consumers must reference-count (deduplicate) repeated store/remove announcements. |
 | `spec_module_path` | no | — | both | Python import path for a custom `OffloadingSpec` not in the built-in registry. Required only when `spec_name` is not built-in (advanced). |

 ## Secondary Tiers
@@ -13,11 +13,7 @@ Install the NIXL library: `uv pip install nixl`, as a quick start on Nvidia plat
 - Refer to [NIXL official repository](https://github.com/ai-dynamo/nixl) for more installation instructions
 - The specified required NIXL version can be found in [requirements/kv_connectors.txt](../../requirements/kv_connectors.txt) and other relevant config files

-For ROCm platform, the [ROCm docker file](../../docker/Dockerfile.rocm) includes RIXL and ucx already.
-
- Refer to [RIXL official repository](https://github.com/rocm/rixl) for more information
- The supportive libraries for RIXL can be found in [requirements/kv_connectors_rocm.txt](../../requirements/kv_connectors_rocm.txt)
- In the future we may remove RIXL from docker image file and users will be able to install from pre-compiled binary packages
+For ROCm, the [ROCm Dockerfile](../../docker/Dockerfile.rocm) builds NIXL and UCX with ROCm support from source.

 For non-cuda platform, please install nixl with ucx build from source, instructed as below.

@@ -315,7 +315,7 @@ vLLM CPU supports data parallel (DP), tensor parallel (TP) and pipeline parallel
 - vLLM CPU supports quantizations:
    - AWQ (x86 only)
    - GPTQ (x86 only)
-    - compressed-tensor INT8 W8A8 (x86, s390x)
+    - compressed-tensor INT8 W8A8 (x86 only)

 ### Why do I see `get_mempolicy: Operation not permitted` when running in Docker?

@@ -11,7 +11,7 @@ Currently, the CPU implementation for s390x architecture supports FP32, BF16 and
 - OS: `Linux`
 - SDK: `gcc/g++ >= 14.0.0` or later with Command Line Tools
 - Instruction Set Architecture (ISA): VXE support is required. Works with Z14 and above.
- Build install python packages: `torchvision`, `llvmlite`, `numba`, `pyarrow (for testing)`, `opencv-headless`
+- Build from source python packages (no pre-built s390x wheels): `torchvision`, `llvmlite`, `numba`, `opencv-python-headless`, `hf-xet`

 --8<-- [end:requirements]
 --8<-- [start:set-up-using-python]
@@ -28,13 +28,24 @@ Install the following packages from the package manager before building the vLLM

 ```bash
 dnf install -y \
-    which procps findutils tar vim git gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
+    which procps findutils tar vim git patch xz ninja-build \
+    gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
    libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
    openssl-devel openblas openblas-devel autoconf automake libtool cmake numpy libsndfile \
    clang llvm-devel llvm-static clang-devel
 ```

-Install rust>=1.80 which is needed for `outlines-core` and `uvloop` python packages installation.
+Build and install `numactl` from source:
+
+```bash
+curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.19.tar.gz
+tar -xvzf v2.0.19.tar.gz
+cd numactl-2.0.19
+./autogen.sh && ./configure && make && make install
+cd ..
+```
+
+Install rust>=1.80 which is needed for `outlines-core`, `uvloop`, and `hf-xet` python packages installation.

 ```bash
 curl https://sh.rustup.rs -sSf | sh -s -- -y && \
@@ -44,26 +55,79 @@ curl https://sh.rustup.rs -sSf | sh -s -- -y && \
 Execute the following commands to build and install vLLM from source.

 !!! tip
-    Please build the following dependencies, `torchvision`, `llvmlite`, `numba`, `llguidance`, `pyarrow`, `opencv-headless` from source before building vLLM.
+    Pre-built wheels are not available for s390x for the following packages. Build them from source before building vLLM: `torchvision`, `llvmlite`, `numba`, `opencv-python-headless`, `hf-xet`.
+    See `docker/Dockerfile.s390x` for exact versions and build commands used in each multi-stage build.
+
+!!! note "LLVM 20 required for llvmlite"
+    `llvmlite v0.47` requires LLVM 20, but UBI 9.6 repos ship LLVM 21 which is
+    not compatible. You must build LLVM 20 from source before building `llvmlite`:
+
+    ```bash
+    curl -LO https://github.com/llvm/llvm-project/releases/download/llvmorg-20.1.8/llvm-project-20.1.8.src.tar.xz
+    tar -xf llvm-project-20.1.8.src.tar.xz
+    cmake -G Ninja -S llvm-project-20.1.8.src/llvm -B llvm-build \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_INSTALL_PREFIX=/opt/llvm20 \
+        -DLLVM_TARGETS_TO_BUILD="SystemZ" \
+        -DLLVM_ENABLE_RTTI=ON \
+        -DLLVM_BUILD_TOOLS=OFF \
+        -DLLVM_BUILD_UTILS=ON \
+        -DLLVM_BUILD_EXAMPLES=OFF \
+        -DLLVM_BUILD_TESTS=OFF \
+        -DLLVM_INCLUDE_TESTS=OFF \
+        -DLLVM_INCLUDE_EXAMPLES=OFF \
+        -DLLVM_INCLUDE_BENCHMARKS=OFF
+    ninja -C llvm-build install
+    ```
+
+    Then build `llvmlite` pointing to LLVM 20:
+
+    ```bash
+    CMAKE_PREFIX_PATH=/opt/llvm20 LLVM_CONFIG=/opt/llvm20/bin/llvm-config \
+        python setup.py bdist_wheel
+    ```

 ```bash
-    uv pip install -v \
-        -r requirements/build/cpu.txt \
-        -r requirements/cpu.txt \
-        --torch-backend cpu \
-        --index-strategy unsafe-best-match && \
-    VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
-        uv pip install dist/*.whl
+uv pip install -v \
+    /path/to/torchvision.whl \
+    /path/to/llvmlite.whl \
+    /path/to/numba.whl \
+    /path/to/opencv_python_headless.whl \
+    /path/to/hf_xet.whl \
+    -r requirements/build/cpu.txt \
+    -r requirements/cpu.txt \
+    --torch-backend cpu \
+    --index-strategy unsafe-best-match && \
+VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
+    uv pip install dist/*.whl
 ```

 ??? console "pip"
    ```bash
-        pip install -v \
-            --extra-index-url https://download.pytorch.org/whl/cpu \
-            -r requirements/build/cpu.txt \
-            -r requirements/cpu.txt \
-        VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
-            pip install dist/*.whl
+    pip install -v \
+        --extra-index-url https://download.pytorch.org/whl/cpu \
+        /path/to/torchvision.whl \
+        /path/to/llvmlite.whl \
+        /path/to/numba.whl \
+        /path/to/opencv_python_headless.whl \
+        /path/to/hf_xet.whl \
+        -r requirements/build/cpu.txt \
+        -r requirements/cpu.txt && \
+    VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
+        pip install dist/*.whl
+    ```
+
+!!! warning "Protobuf workaround for s390x"
+    The C++ protobuf extension crashes on s390x. After installation, set the
+    following environment variable and remove the C++ extensions:
+
+    ```bash
+    export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+    # Remove C++ protobuf extensions that crash on s390x
+    SITE_PKGS=$(python -c "import site; print(site.getsitepackages()[0])")
+    rm -rf "$SITE_PKGS/google/_upb/"*.so \
+           "$SITE_PKGS/google/protobuf/pyext/"*.so 2>/dev/null || true
    ```

 --8<-- [end:build-wheel-from-source]
@@ -80,19 +144,20 @@ docker build -f docker/Dockerfile.s390x \

 # Launch OpenAI server
 docker run --rm \
-    --privileged true \
+    --security-opt seccomp=unconfined \
+    --cap-add SYS_NICE \
    --shm-size 4g \
    -p 8000:8000 \
    -e VLLM_CPU_KVCACHE_SPACE=<KV cache space> \
    -e VLLM_CPU_OMP_THREADS_BIND=<CPU cores for inference> \
    vllm-cpu-env \
    --model meta-llama/Llama-3.2-1B-Instruct \
-    --dtype float \
+    --dtype bfloat16 \
    other vLLM OpenAI server arguments
 ```

 !!! tip
-    An alternative of `--privileged true` is `--cap-add SYS_NICE --security-opt seccomp=unconfined`.
+    Alternatively, `--privileged=true` also works but is broader and not generally recommended.

 --8<-- [end:build-image-from-source]
 --8<-- [start:extra-information]
@@ -27,7 +27,7 @@ Currently, there are no pre-built XPU wheels.

 - First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers).
 - Second, install Python packages for vLLM XPU backend building (Intel OneAPI dependencies are installed automatically as part of `torch-xpu`, see [PyTorch XPU get started](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html)):
- Start from vllm-xpu-kernels v0.1.10, we recommend user upgrade driver to [compute runtime 26.18](https://github.com/intel/compute-runtime/releases/tag/26.14.37833.4) release, to avoid potential compatibility issue.
+- Start from vllm-xpu-kernels v0.1.10, we recommend user upgrade driver to [compute runtime 26.18](https://github.com/intel/compute-runtime/releases/tag/26.18.38308.1) release, to avoid potential compatibility issue.

 ```bash
 git clone https://github.com/vllm-project/vllm.git
@@ -58,7 +58,40 @@ VLLM_TARGET_DEVICE=xpu pip install --no-build-isolation -e . -v
 --8<-- [end:build-wheel-from-source]
 --8<-- [start:pre-built-images]

-Currently, we release prebuilt XPU images at docker [hub](https://hub.docker.com/r/intel/vllm/tags) based on vLLM released version. For more information, please refer release [note](https://github.com/intel/ai-containers/blob/main/vllm).
+vLLM offers official Docker images for deployment.
+The images can be used to run OpenAI compatible server and are available on Docker Hub as [vllm/vllm-openai-xpu](https://hub.docker.com/r/vllm/vllm-openai-xpu/tags).
+
+- `vllm/vllm-openai-xpu:latest` — stable release, available starting from v0.26.0
+- `vllm/vllm-openai-xpu:nightly` — preview build from the latest development branch, use this if you want the latest features and fixes
+
+```bash
+docker run --rm \
+    --network=host \
+    --device /dev/dri:/dev/dri \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=$HF_TOKEN" \
+    --ipc=host \
+    --privileged \
+    vllm/vllm-openai-xpu:<tag> \
+    --model Qwen/Qwen3-0.6B
+```
+
+To use the docker image as base for development, you can launch it in interactive session through overriding the entrypoint.
+
+???+ console "Commands"
+    ```bash
+    docker run --rm -it \
+        --network=host \
+        --device /dev/dri:/dev/dri \
+        -v /dev/dri/by-path:/dev/dri/by-path \
+        -v ~/.cache/huggingface:/root/.cache/huggingface \
+        --env "HF_TOKEN=$HF_TOKEN" \
+        --ipc=host \
+        --privileged \
+        --entrypoint /bin/bash \
+        vllm/vllm-openai-xpu:<tag>
+    ```

 --8<-- [end:pre-built-images]
 --8<-- [start:build-image-from-source]
@@ -65,6 +65,15 @@ This guide will help you quickly get started with vLLM to perform:
    !!! tip
        A nightly Docker image is also available as [vllm/vllm-openai-rocm:nightly](https://hub.docker.com/r/vllm/vllm-openai-rocm/tags) for testing the latest development builds.

+=== "Intel GPU"
+
+    vLLM supports Intel GPUs through the XPU backend. Pre-built XPU wheels will be available soon.
+
+    Official Docker images for Intel GPUs are added to the vLLM release starting from v0.26.0. Nightly Docker image is also available as [vllm/vllm-openai-xpu:nightly](https://hub.docker.com/r/vllm/vllm-openai-xpu/tags).
+
+    !!! tip
+        For more detailed instructions, including building from source and Docker image setup, please refer to the [GPU installation guide](installation/gpu.md) and select the "Intel XPU" tab.
+
 === "Google TPU"

    To run vLLM on Google TPUs, you need to install the `vllm-tpu` package.
@@ -0,0 +1,3 @@
+// Reo.Dev documentation tracking
+// https://docs.reo.dev/integrations/input-sources/developer-insights/documentation
+!function(){var e,t,n;e="d5c4337961ef0ac",t=function(){Reo.init({clientID:"d5c4337961ef0ac", enableThirdPartyTracking: true})},(n=document.createElement("script")).src="https://static.reo.dev/"+e+"/reo.js",n.defer=!0,n.onload=t,document.head.appendChild(n)}();
@@ -67,7 +67,7 @@ The Transcriptions API supports uploading audio files in various formats includi
 - `response_format`: Format of the response ("json", "text") (optional)
 - `temperature`: Sampling temperature between 0 and 1 (optional)

-For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/protocol.py#L2182).
+For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/speech_to_text/transcription/protocol.py).

 **Response Format:**

@@ -160,3 +160,4 @@ extra_javascript:
  - https://unpkg.com/mathjax@3.2.2/es5/tex-mml-chtml.js
  - mkdocs/javascript/edit_and_feedback.js
  - mkdocs/javascript/slack_and_forum.js
+  - mkdocs/javascript/reo.js
@@ -7,7 +7,7 @@ requires = [
    "setuptools>=77.0.3,<81.0.0",
    "setuptools-scm>=8.0",
    "setuptools-rust>=1.9.0",
-    "torch == 2.11.0",
+    "torch == 2.13.0",
    "wheel",
    "jinja2",
 ]
@@ -4,8 +4,8 @@ packaging>=24.2
 setuptools==77.0.3 # this version can reuse CMake build dir
 setuptools-scm>=8
 setuptools-rust>=1.9.0
-torch==2.11.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64"
-torch==2.11.0; platform_system == "Darwin" or platform_machine == "ppc64le"  or platform_machine == "riscv64"
+torch==2.13.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64"
+torch==2.13.0; platform_system == "Darwin" or platform_machine == "ppc64le"  or platform_machine == "riscv64"
 wheel
 jinja2>=3.1.6
 regex
@@ -5,7 +5,7 @@ packaging>=24.2
 setuptools>=77.0.3,<81.0.0
 setuptools-scm>=8
 setuptools-rust>=1.9.0
-torch==2.11.0
+torch==2.13.0
 wheel
 jinja2>=3.1.6
 regex
@@ -6,8 +6,8 @@ setuptools==77.0.3 # this version can reuse CMake build dir
 numba == 0.65.0; platform_machine != "s390x" # Required for N-gram speculative decoding

 # Dependencies for CPUs
-torch==2.11.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64"
-torch==2.11.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64"
+torch==2.13.0+cpu; platform_machine == "x86_64" or platform_machine == "s390x" or platform_machine == "aarch64"
+torch==2.13.0; platform_system == "Darwin" or platform_machine == "ppc64le" or platform_machine == "riscv64"

 # required for the image processor of minicpm-o-2_6, this must be updated alongside torch
 torchaudio; platform_machine != "s390x" and platform_machine != "riscv64"
@@ -4,18 +4,18 @@
 numba == 0.65.0 # Required for N-gram speculative decoding

 # Dependencies for NVIDIA GPUs
-torch==2.11.0
+torch==2.13.0
 torchaudio==2.11.0
 # These must be updated alongside torch
-torchvision==0.26.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
+torchvision==0.28.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 torchcodec >= 0.14
 PyNvVideoCodec==2.0.4
 # FlashInfer should be updated together with the Dockerfile
 # flashinfer-cubin is not on PyPI since 0.6.14; setup.py excludes it from
 # install_requires so the published wheel does not carry an unresolvable pin
 --extra-index-url https://flashinfer.ai/whl/
-flashinfer-python==0.6.14
-flashinfer-cubin==0.6.14
+flashinfer-python==0.6.15.post1
+flashinfer-cubin==0.6.15.post1
 apache-tvm-ffi==0.1.10
 tilelang==0.1.9
 nvidia-cudnn-frontend>=1.19.1
@@ -26,7 +26,7 @@ fastsafetensors >= 0.3.2

 # QuACK and Cutlass DSL for FA4 (cute-DSL implementation)
 nvidia-cutlass-dsl[cu13]==4.6.0
-quack-kernels>=0.4.0  # Required for tml-fa4
+quack-kernels>=0.6.1  # Required for CUTLASS DSL 4.6 by MSA

 # Tokenspeed_MLA for faster mla with spec decode
 tokenspeed-mla==0.1.8; platform_system == "Linux"
@@ -1107,7 +1107,7 @@ tokenizers==0.22.2
    #   -r requirements/test/../common.txt
    #   -r requirements/test/cuda.in
    #   transformers
-torch==2.11.0+cpu
+torch==2.13.0+cpu
    # via
    #   -r requirements/test/cuda.in
    #   accelerate
@@ -1134,7 +1134,7 @@ torchaudio==2.11.0+cpu
    #   vocos
 torchcodec==0.14.0+cpu
    # via -r requirements/test/cuda.in
-torchvision==0.26.0+cpu
+torchvision==0.28.0+cpu
    # via
    #   -r requirements/test/cuda.in
    #   open-clip-torch
@@ -28,9 +28,9 @@ soundfile # required for audio tests
 jiwer # required for audio tests
 tblib # for pickling test exceptions
 timm >=1.0.17 # required for internvl and gemma3n-mm test
-torch==2.11.0
+torch==2.13.0
 torchaudio==2.11.0
-torchvision==0.26.0
+torchvision==0.28.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[image,audio] >= 1.11.5 # required for voxtral test
@@ -159,7 +159,7 @@ cuda-bindings==13.0.3
    # via torch
 cuda-pathfinder==1.3.3
    # via cuda-bindings
-cuda-toolkit==13.0.2
+cuda-toolkit==13.0.3.0
    # via torch
 cupy-cuda12x==13.6.0
    # via ray
@@ -599,7 +599,7 @@ numpy==2.2.6
    #   tritonclient
    #   vocos
    #   xgrammar
-nvidia-cublas==13.1.0.3
+nvidia-cublas==13.1.1.3
    # via
    #   cuda-toolkit
    #   nvidia-cudnn-cu13
@@ -607,10 +607,12 @@ nvidia-cublas==13.1.0.3
 nvidia-cuda-cupti==13.0.85
    # via cuda-toolkit
 nvidia-cuda-nvrtc==13.0.88
-    # via cuda-toolkit
+    # via
+    #   cuda-toolkit
+    #   nvidia-cublas
 nvidia-cuda-runtime==13.0.96
    # via cuda-toolkit
-nvidia-cudnn-cu13==9.19.0.56
+nvidia-cudnn-cu13==9.20.0.48
    # via torch
 nvidia-cufft==12.0.0.61
    # via cuda-toolkit
@@ -624,9 +626,9 @@ nvidia-cusparse==12.6.3.3
    # via
    #   cuda-toolkit
    #   nvidia-cusolver
-nvidia-cusparselt-cu13==0.8.0
+nvidia-cusparselt-cu13==0.8.1
    # via torch
-nvidia-nccl-cu13==2.28.9
+nvidia-nccl-cu13==2.29.7
    # via torch
 nvidia-nvjitlink==13.0.88
    # via
@@ -1202,7 +1204,7 @@ tokenizers==0.22.2
    #   -r requirements/test/../common.txt
    #   -r requirements/test/cuda.in
    #   transformers
-torch==2.11.0+cu130
+torch==2.13.0+cu130
    # via
    #   -c requirements/cuda.txt
    #   -r requirements/test/cuda.in
@@ -1233,7 +1235,7 @@ torchcodec==0.14.0+cu130
    # via
    #   -c requirements/cuda.txt
    #   -r requirements/test/cuda.in
-torchvision==0.26.0+cu130
+torchvision==0.28.0+cu130
    # via
    #   -c requirements/cuda.txt
    #   -r requirements/test/cuda.in
@@ -1270,7 +1272,7 @@ transformers==5.13.1
    #   xgrammar
 transformers-stream-generator==0.0.5
    # via -r requirements/test/cuda.in
-triton==3.6.0
+triton==3.7.1
    # via
    #   torch
    #   xgrammar
@@ -12,4 +12,4 @@ ray[data]
 setuptools==78.1.0
 setuptools-rust>=1.9.0
 nixl==0.3.0
-tpu-inference==0.24.0
+tpu-inference==0.25.0
@@ -3446,7 +3446,6 @@ dependencies = [
 "base64 0.22.1",
 "bytes",
 "encoding_rs",
- "futures-channel",
 "futures-core",
 "futures-util",
 "h2",
@@ -4876,6 +4875,7 @@ dependencies = [
 "futures-core",
 "pin-project-lite",
 "tokio",
+ "tokio-util",
 ]

 [[package]]
@@ -4937,9 +4937,9 @@ dependencies = [

 [[package]]
 name = "tonic"
-version = "0.14.5"
+version = "0.14.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fec7c61a0695dc1887c1b53952990f3ad2e3a31453e1f49f10e75424943a93ec"
+checksum = "ac2a5518c70fa84342385732db33fb3f44bc4cc748936eb5833d2df34d6445ef"
 dependencies = [
 "async-trait",
 "axum",
@@ -4966,9 +4966,9 @@ dependencies = [

 [[package]]
 name = "tonic-build"
-version = "0.14.5"
+version = "0.14.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1882ac3bf5ef12877d7ed57aad87e75154c11931c2ba7e6cde5e22d63522c734"
+checksum = "c68f61875ac5293cf72e6c8cf0158086428c82c37229e98c840878f1706b0322"
 dependencies = [
 "prettyplease",
 "proc-macro2",
@@ -4977,10 +4977,23 @@ dependencies = [
 ]

 [[package]]
-name = "tonic-prost"
-version = "0.14.5"
+name = "tonic-health"
+version = "0.14.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a55376a0bbaa4975a3f10d009ad763d8f4108f067c7c2e74f3001fb49778d309"
+checksum = "fcfab99db777fba2802f0dfa861d1628d1ae916fb199d29819941f139ae85082"
+dependencies = [
+ "prost",
+ "tokio",
+ "tokio-stream",
+ "tonic",
+ "tonic-prost",
+]
+
+[[package]]
+name = "tonic-prost"
+version = "0.14.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "50849f68853be452acf590cde0b146665b8d507b3b8af17261df47e02c209ea0"
 dependencies = [
 "bytes",
 "prost",
@@ -4989,9 +5002,9 @@ dependencies = [

 [[package]]
 name = "tonic-prost-build"
-version = "0.14.5"
+version = "0.14.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f3144df636917574672e93d0f56d7edec49f90305749c668df5101751bb8f95a"
+checksum = "654e5643eff75d7f8c99197ce1440ed19a3474eada74c12bbac488b2cafdae27"
 dependencies = [
 "prettyplease",
 "proc-macro2",
@@ -5484,10 +5497,13 @@ dependencies = [
 "serde",
 "serde_json",
 "thiserror 2.0.18",
+ "thiserror-ext",
 "tiktoken-rs 0.9.1",
 "tokenizers",
 "tokio",
 "tokio-stream",
+ "tracing",
+ "tracing-subscriber",
 "url",
 "uuid",
 ]
@@ -5730,6 +5746,7 @@ dependencies = [
 "tokio-stream",
 "tokio-util",
 "tonic",
+ "tonic-health",
 "tonic-prost",
 "tonic-prost-build",
 "tower",
@@ -6321,9 +6338,9 @@ checksum = "9edde0db4769d2dc68579893f2306b26c6ecfbe0ef499b013d731b7b9247e0b9"

 [[package]]
 name = "xgrammar-structural-tag"
-version = "0.1.0+xgrammar.0.2.2.4d145cc"
+version = "0.2.0+xgrammar.0.2.4.dd729e7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2436dea2393d55a3b188588aa300c5a8afe8f45a77da52c611fb4498a6c876e6"
+checksum = "d4d24c842efc3c24e9756aa426d530cbdac0980e49af223cb384e276e981ca0a"
 dependencies = [
 "auto_impl",
 "serde",
@@ -118,10 +118,11 @@ tokio = { version = "1.47.1", features = [
 tokio-openssl = "0.6"
 tokio-stream = "0.1"
 tokio-util = { version = "0.7.18", features = ["rt"] }
-tonic = "0.14.5"
-tonic-build = "0.14.5"
-tonic-prost = "0.14.5"
-tonic-prost-build = "0.14.5"
+tonic = "0.14.6"
+tonic-build = "0.14.6"
+tonic-health = "0.14.6"
+tonic-prost = "0.14.6"
+tonic-prost-build = "0.14.6"
 tool-parser = "1.2.0"
 tower = { version = "0.5.3", features = ["util"] }
 tower-http = { version = "0.6.8", features = ["cors", "trace"] }
@@ -143,7 +144,7 @@ vllm-server = { path = "src/server" }
 vllm-text = { path = "src/text" }
 vllm-tokenizer = { path = "src/tokenizer" }
 winnow = { version = "1.0.2", features = ["simd"] }
-xgrammar-structural-tag = "0.1.0"
+xgrammar-structural-tag = "0.2.0"
 zeromq = { version = "0.6.0", default-features = false, features = [
    "tokio-runtime",
    "all-transport",
@@ -14,6 +14,10 @@ service Generate {
  rpc GenerateStream (GenerateRequest) returns (stream GenerateResponse) {}
 }

+service Control {
+  rpc Abort (AbortRequest) returns (AbortResponse) {}
+}
+
 // ======================================================================================
 // Generate Request
 // ======================================================================================
@@ -201,3 +205,12 @@ message TokenIds {
  repeated uint32 ids = 1;
 }

+// ======================================================================================
+// Control
+// ======================================================================================
+
+message AbortRequest {
+  repeated string request_ids = 1;
+}
+
+message AbortResponse {}
@@ -20,16 +20,19 @@ mimalloc.workspace = true
 rand.workspace = true
 rand_distr.workspace = true
 rayon.workspace = true
-reqwest = { workspace = true, features = ["json", "stream", "blocking", "http2"] }
+reqwest = { workspace = true, features = ["json", "stream", "http2"] }
 rlimit.workspace = true
 rustc-hash.workspace = true
 serde = { workspace = true, features = ["rc"] }
 serde_json = { workspace = true, features = ["raw_value"] }
 thiserror.workspace = true
+thiserror-ext.workspace = true
 tiktoken-rs.workspace = true
 tokenizers.workspace = true
 tokio.workspace = true
 tokio-stream.workspace = true
+tracing.workspace = true
+tracing-subscriber.workspace = true
 url.workspace = true
 uuid.workspace = true

@@ -158,9 +158,10 @@ impl PoolingBackend {
                // (mirrors Python async_request_vllm_rerank).
                if let Some(ref list) = input.prompt_list {
                    if list.len() < 2 {
-                        eprintln!(
-                            "WARNING: vllm-rerank request has no documents \
-                             (prompt_list needs [query, doc, ...])"
+                        tracing::warn!(
+                            backend = "vllm-rerank",
+                            inputs = list.len(),
+                            "rerank request has no documents"
                        );
                    }
                    let query = list.first().map(|s| s.as_ref()).unwrap_or("");
@@ -175,10 +176,10 @@ impl PoolingBackend {
                    // Legacy path: text prompt as query, documents via --extra-body.
                    let query = input.prompt.as_ref();
                    if query.is_empty() && input.prompt_token_ids.is_some() {
-                        eprintln!(
-                            "WARNING: vllm-rerank received empty query (random dataset uses \
-                             token IDs only). Use --dataset-name random-rerank for meaningful \
-                             rerank benchmarks."
+                        tracing::warn!(
+                            backend = "vllm-rerank",
+                            dataset = "random",
+                            "rerank request has an empty query; use the random-rerank dataset"
                        );
                    }
                    serde_json::json!({
@@ -6,6 +6,7 @@ use std::sync::Arc;
 use std::time::Instant;

 use indicatif::{ProgressBar, ProgressStyle};
+use thiserror_ext::AsReport as _;
 use tokio::sync::Semaphore;

 use crate::backends::{RequestFuncInput, RequestFuncOutput, get_backend};
@@ -72,12 +73,12 @@ pub fn pre_resolve_dns(
            v4.extend(v6);
            if !v4.is_empty() {
                let ips: Vec<_> = v4.iter().map(|a| a.ip()).collect();
-                println!("Pre-resolved {host} -> {ips:?}");
+                tracing::info!(host, addresses = ?ips, "pre-resolved benchmark endpoint DNS");
                builder = builder.resolve_to_addrs(host, &v4);
            }
        }
        Err(e) => {
-            eprintln!("Warning: DNS pre-resolution for '{host}' failed: {e}");
+            tracing::warn!(host, error = %e.as_report(), "failed to pre-resolve benchmark endpoint DNS");
        }
    }

@@ -346,10 +347,14 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    let (model_id, model_name) = if let Some(ref m) = config.model {
        (m.clone(), config.model_name.clone())
    } else {
-        println!("Model not specified, fetching first model from server...");
+        tracing::info!(base_url = %config.base_url, "fetching first model from server");
        let (name, id) =
            get_first_model_from_server(&config.base_url, &client, &config.extra_headers).await?;
-        println!("First model name: {name}, first model id: {id}");
+        tracing::info!(
+            model_name = name,
+            model_id = id,
+            "selected first model from server"
+        );
        (id, Some(name))
    };

@@ -358,10 +363,10 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
        None
    } else {
        let tid = config.tokenizer_id.as_deref().unwrap_or(&model_id);
-        println!("Loading tokenizer: {tid}");
+        tracing::info!(tokenizer = tid, "loading tokenizer");
        let server_info = Some((config.base_url.as_str(), model_id.as_str()));
-        let t = crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info)?;
-        println!("Tokenizer loaded successfully.");
+        let t =
+            crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info).await?;
        Some(t)
    };
    let has_tokenizer = tokenizer.is_some();
@@ -421,7 +426,12 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            config.num_prompts, config.random_batch_size, config.is_reranker,
        ),
    };
-    println!("Generating {dataset_label}...");
+    tracing::info!(
+        dataset = ?config.dataset_name,
+        prompts = config.num_prompts,
+        description = %dataset_label,
+        "generating benchmark dataset"
+    );
    let gen_start = Instant::now();

    let mut input_requests = match config.dataset_name {
@@ -472,7 +482,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            let path = match config.dataset_path.as_deref() {
                Some(p) => p,
                None => {
-                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset()?;
+                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset().await?;
                    downloaded.as_str()
                }
            };
@@ -512,7 +522,8 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                None => {
                    downloaded = crate::datasets::speed_bench::download_speed_bench(
                        config.speed_bench_config,
-                    )?;
+                    )
+                    .await?;
                    downloaded.as_str()
                }
            };
@@ -543,7 +554,8 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                    config.hf_subset.as_deref(),
                    config.hf_split.as_deref(),
                    config.num_prompts,
-                )?;
+                )
+                .await?;
            crate::datasets::hf_dataset::load_hf_dataset(
                tok,
                &downloaded_path,
@@ -608,18 +620,19 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    };

    let gen_elapsed = gen_start.elapsed();
-    println!(
-        "Generated {} prompts in {:.2}s",
-        input_requests.len(),
-        gen_elapsed.as_secs_f64()
+    tracing::info!(
+        prompts = input_requests.len(),
+        elapsed_seconds = gen_elapsed.as_secs_f64(),
+        "generated benchmark dataset"
    );

    let filtered_count =
        filter_requests_by_max_model_len(&mut input_requests, config.max_model_len);
    if filtered_count > 0 {
-        println!(
-            "Filtered {filtered_count} prompt(s) above --max-model-len {}.",
-            config.max_model_len.unwrap()
+        tracing::info!(
+            filtered_prompts = filtered_count,
+            max_model_len = config.max_model_len.unwrap(),
+            "filtered prompts above maximum model length"
        );
    }
    if input_requests.is_empty() {
@@ -670,7 +683,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {

    // Ready check
    if config.ready_check_timeout_sec > 0 {
-        println!("Starting initial single prompt test run...");
+        tracing::info!("starting initial single-prompt test run");
        let test_output = wait_for_endpoint(
            config.backend,
            &client,
@@ -685,7 +698,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                test_output.error
            )));
        }
-        println!("Initial test run completed.");
+        tracing::info!("initial single-prompt test run completed");
    }

    // Verify and fix prompt token lengths against the server's /tokenize endpoint.
@@ -703,12 +716,15 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
        DatasetName::Random | DatasetName::PrefixRepetition
    );
    if verifiable_dataset && has_token_ids && !config.backend.is_pooling() {
-        println!("Using prompt_token_ids, skipping server-side tokenizer verification.");
+        tracing::info!(
+            reason = "prompt_token_ids",
+            "skipping server tokenizer verification"
+        );
    }
    if verifiable_dataset && !has_token_ids && !config.backend.is_pooling() {
        let cache_key = tokenizer_verify_cache_key(&config.base_url, &model_id);
        if is_tokenizer_verified(&cache_key) {
-            println!("Tokenizer verified in previous run (cached), skipping verification.");
+            tracing::info!(reason = "cached", "skipping server tokenizer verification");
        } else {
            let num_special =
                tokenizer.as_ref().map(|t| t.num_special_tokens_to_add()).unwrap_or(0);
@@ -723,14 +739,17 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            .await?
            {
                SampleVerifyOutcome::Passed => {
-                    println!("Sample verification passed, skipping full verification.");
+                    tracing::info!("tokenizer sample verification passed");
                    mark_tokenizer_verified(&cache_key);
                }
                SampleVerifyOutcome::Skipped(reason) => {
-                    println!("Server /tokenize unavailable ({reason}), skipping verification.");
+                    tracing::warn!(
+                        reason = %reason,
+                        "server tokenizer unavailable; skipping prompt verification"
+                    );
                }
                SampleVerifyOutcome::Mismatch => {
-                    println!("Sample verification found mismatch, running full verify+fix...");
+                    tracing::warn!("tokenizer sample mismatch; verifying and fixing all prompts");
                    match verify_and_fix_prompt_lengths(
                        &client,
                        &config.base_url,
@@ -742,16 +761,16 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                    .await
                    {
                        Ok(()) => {
-                            println!(
-                                "All {} prompts verified: exact token length match.",
-                                input_requests.len()
+                            tracing::info!(
+                                prompts = input_requests.len(),
+                                "verified exact prompt token lengths"
                            );
                            mark_tokenizer_verified(&cache_key);
                        }
                        Err(BenchError::TokenizeUnavailable(reason)) => {
-                            println!(
-                                "Server /tokenize became unavailable during verification \
-                                 ({reason}); proceeding with client-side token counts."
+                            tracing::warn!(
+                                reason = %reason,
+                                "server tokenizer became unavailable; using client token counts"
                            );
                        }
                        Err(e) => return Err(e),
@@ -763,7 +782,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {

    // Warmup
    if config.num_warmups > 0 {
-        println!("Warming up with {} requests...", config.num_warmups);
+        tracing::info!(requests = config.num_warmups, "starting benchmark warmup");
        run_warmup(
            config.backend,
            &client,
@@ -776,7 +795,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            config.disable_tqdm,
        )
        .await;
-        println!("Warmup run completed.");
+        tracing::info!(requests = config.num_warmups, "benchmark warmup completed");
    }

    // Start profiler if requested (immediate mode — no batch threshold)
@@ -814,28 +833,22 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    let spec_decode_before =
        fetch_spec_decode_metrics(&config.base_url, &client, &config.extra_headers).await;
    if spec_decode_before.is_some() {
-        println!("Speculative decoding detected, will collect metrics.");
+        tracing::info!("detected speculative decoding; collecting metrics");
    }

    // Main benchmark
-    println!("Starting main benchmark run...");
    let distribution = if config.burstiness == 1.0 {
        "Poisson process"
    } else {
        "Gamma distribution"
    };
-    println!(
-        "Traffic request rate: {}",
-        if config.request_rate.is_infinite() {
-            "inf".to_string()
-        } else {
-            format!("{}", config.request_rate)
-        }
-    );
-    println!("Burstiness factor: {} ({distribution})", config.burstiness);
-    println!(
-        "Maximum request concurrency: {}",
-        config.max_concurrency.unwrap_or(config.num_prompts)
+    tracing::info!(
+        request_rate = config.request_rate,
+        burstiness = config.burstiness,
+        distribution,
+        max_concurrency = config.max_concurrency.unwrap_or(config.num_prompts),
+        prompts = config.num_prompts,
+        "starting main benchmark run"
    );

    // Pre-assign LoRA adapters to each request (None when --lora-modules not set).
@@ -847,11 +860,11 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    );
    if let (Some(modules), Some(_)) = (config.lora_modules.as_ref(), lora_assignments.as_ref()) {
        let names: Vec<&str> = modules.iter().map(|s| s.as_ref()).collect();
-        println!(
-            "LoRA adapters ({}): {:?} [assignment={:?}]",
-            modules.len(),
-            names,
-            config.lora_assignment
+        tracing::info!(
+            adapters = modules.len(),
+            names = ?names,
+            assignment = ?config.lora_assignment,
+            "assigned LoRA adapters"
        );
    }

@@ -1125,7 +1138,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    if let Some((cancel_tx, task)) = profile_task {
        let _ = cancel_tx.send(());
        if let Err(e) = task.await {
-            eprintln!("WARNING: Profile background task failed: {e}");
+            tracing::error!(error = %e.as_report(), "profiler background task failed");
        }
    }

@@ -1289,12 +1302,14 @@ pub(crate) async fn start_profiler_immediate(
    base_url: &str,
    extra_headers: &Option<std::collections::HashMap<String, String>>,
 ) {
-    println!("Starting profiler...");
    let profile_url = format!("{base_url}/start_profile");
+    tracing::info!(url = %profile_url, "starting profiler");
    match send_profile_request(client, &profile_url, extra_headers).await {
-        Ok(true) => println!("Profiler started"),
-        Ok(false) => eprintln!("WARNING: Profiler start request returned non-success"),
-        Err(e) => eprintln!("WARNING: Failed to start profiler: {e}"),
+        Ok(true) => tracing::info!(url = %profile_url, "profiler started"),
+        Ok(false) => tracing::warn!(url = %profile_url, "profiler start request was unsuccessful"),
+        Err(e) => {
+            tracing::warn!(url = %profile_url, error = %e.as_report(), "failed to start profiler")
+        }
    }
 }

@@ -1304,12 +1319,14 @@ pub(crate) async fn stop_profiler_immediate(
    base_url: &str,
    extra_headers: &Option<std::collections::HashMap<String, String>>,
 ) {
-    println!("Stopping profiler...");
    let profile_url = format!("{base_url}/stop_profile");
+    tracing::info!(url = %profile_url, "stopping profiler");
    match send_profile_request(client, &profile_url, extra_headers).await {
-        Ok(true) => println!("Profiler stopped"),
-        Ok(false) => eprintln!("WARNING: Profiler stop request returned non-success"),
-        Err(e) => eprintln!("WARNING: Failed to stop profiler: {e}"),
+        Ok(true) => tracing::info!(url = %profile_url, "profiler stopped"),
+        Ok(false) => tracing::warn!(url = %profile_url, "profiler stop request was unsuccessful"),
+        Err(e) => {
+            tracing::warn!(url = %profile_url, error = %e.as_report(), "failed to stop profiler")
+        }
    }
 }

@@ -1371,25 +1388,30 @@ pub(crate) async fn profile_on_batch_threshold(
    duration_secs: f64,
    mut cancel_rx: tokio::sync::oneshot::Receiver<()>,
 ) {
-    println!(
-        "Waiting for batch size >= {threshold} before starting profiler \
-         (will capture {duration_secs}s)..."
+    tracing::info!(
+        threshold,
+        duration_seconds = duration_secs,
+        "waiting for profiler batch threshold"
    );

    loop {
        if let Some(running) = fetch_num_requests_running(client, base_url).await
            && running >= threshold
        {
-            println!("Batch size {running} >= {threshold}, starting profiler...");
+            tracing::info!(
+                running_requests = running,
+                threshold,
+                "profiler batch threshold reached"
+            );
            break;
        }
        // Wait 500ms or until the benchmark signals cancellation
        tokio::select! {
            _ = tokio::time::sleep(std::time::Duration::from_millis(500)) => {}
            _ = &mut cancel_rx => {
-                eprintln!(
-                    "NOTE: Benchmark finished before batch threshold {threshold} was reached; \
-                     profiling skipped."
+                tracing::warn!(
+                    threshold,
+                    "benchmark finished before profiler batch threshold; skipping profiling"
                );
                return;
            }
@@ -1398,13 +1420,13 @@ pub(crate) async fn profile_on_batch_threshold(

    let start_url = format!("{base_url}/start_profile");
    match send_profile_request(client, &start_url, extra_headers).await {
-        Ok(true) => println!("Profiler started"),
+        Ok(true) => tracing::info!(url = %start_url, "profiler started"),
        Ok(false) => {
-            eprintln!("WARNING: Profiler start request returned non-success");
+            tracing::warn!(url = %start_url, "profiler start request was unsuccessful");
            return;
        }
        Err(e) => {
-            eprintln!("WARNING: Failed to start profiler: {e}");
+            tracing::warn!(url = %start_url, error = %e.as_report(), "failed to start profiler");
            return;
        }
    }
@@ -1413,15 +1435,17 @@ pub(crate) async fn profile_on_batch_threshold(
    tokio::select! {
        _ = tokio::time::sleep(std::time::Duration::from_secs_f64(duration_secs)) => {}
        _ = &mut cancel_rx => {
-            println!("Benchmark finished, stopping profiler early...");
+            tracing::info!("benchmark finished; stopping profiler early");
        }
    }

    let stop_url = format!("{base_url}/stop_profile");
    match send_profile_request(client, &stop_url, extra_headers).await {
-        Ok(true) => println!("Profiler stopped after capturing"),
-        Ok(false) => eprintln!("WARNING: Profiler stop request returned non-success"),
-        Err(e) => eprintln!("WARNING: Failed to stop profiler: {e}"),
+        Ok(true) => tracing::info!(url = %stop_url, "profiler stopped after capture"),
+        Ok(false) => tracing::warn!(url = %stop_url, "profiler stop request was unsuccessful"),
+        Err(e) => {
+            tracing::warn!(url = %stop_url, error = %e.as_report(), "failed to stop profiler")
+        }
    }
 }

@@ -1502,10 +1526,11 @@ async fn verify_and_fix_prompt_lengths(
                let excess = tokens.len().saturating_sub(expected_input_len);
                let compensate = if excess > 0 && last_excess == Some(excess) {
                    if _iter == 1 {
-                        eprintln!(
-                            "Prompt {i}: server consistently adds {excess} extra token(s) \
-                             (likely BOS), compensating target to {}.",
-                            expected_input_len.saturating_sub(excess),
+                        tracing::warn!(
+                            prompt_index = i,
+                            extra_tokens = excess,
+                            adjusted_target = expected_input_len.saturating_sub(excess),
+                            "server consistently adds prompt tokens; compensating verification target"
                        );
                    }
                    excess
@@ -1563,7 +1588,10 @@ async fn verify_and_fix_prompt_lengths(

    let fc = fixed_count.load(std::sync::atomic::Ordering::Relaxed);
    if fc > 0 {
-        println!("Fixed {fc} prompt(s) via server tokenize/detokenize convergence.");
+        tracing::info!(
+            fixed_prompts = fc,
+            "fixed prompt lengths using server tokenizer"
+        );
    }

    Ok(())
@@ -1818,7 +1846,7 @@ async fn sample_verify_prompts(
    let tokenize_url = format!("{base_url}/tokenize");
    let api_key = std::env::var("OPENAI_API_KEY").ok();

-    println!("Sampling {sample_size} prompts for verification...");
+    tracing::info!(sample_size, "sampling prompts for tokenizer verification");

    for (i, request) in requests.iter().enumerate().take(sample_size) {
        let tokens = match server_tokenize(
@@ -1841,9 +1869,11 @@ async fn sample_verify_prompts(

        let expected = request.prompt_len + num_special;
        if tokens.len() != expected {
-            println!(
-                "Prompt {i}: expected {expected} tokens, server returned {}",
-                tokens.len()
+            tracing::warn!(
+                prompt_index = i,
+                expected_tokens = expected,
+                actual_tokens = tokens.len(),
+                "tokenizer verification sample mismatch"
            );
            return Ok(SampleVerifyOutcome::Mismatch);
        }
@@ -288,10 +288,18 @@ impl BenchConfig {
                    }
                    Some(other) => {
                        // extra_body was not an object — just use sampling params
-                        eprintln!(
-                            "Warning: --extra-body is not a JSON object, sampling params may be lost"
+                        let value_type = match &other {
+                            serde_json::Value::Null => "null",
+                            serde_json::Value::Bool(_) => "boolean",
+                            serde_json::Value::Number(_) => "number",
+                            serde_json::Value::String(_) => "string",
+                            serde_json::Value::Array(_) => "array",
+                            serde_json::Value::Object(_) => unreachable!(),
+                        };
+                        tracing::warn!(
+                            value_type,
+                            "sampling parameters may be lost because --extra-body is not a JSON object"
                        );
-                        let _ = other;
                        sampling_params
                    }
                    None => sampling_params,
@@ -489,9 +497,9 @@ impl BenchConfig {
                _ => {}
            }
            if !args.skip_chat_template {
-                eprintln!(
-                    "NOTE: client-side chat template rendering is not supported; custom \
-                     dataset prompts are sent raw (equivalent to --skip-chat-template)."
+                tracing::warn!(
+                    dataset = "custom",
+                    "client-side chat template rendering is unsupported; sending prompts raw"
                );
            }
        }
@@ -570,9 +578,10 @@ impl BenchConfig {
            }

            if ignore_eos {
-                eprintln!(
-                    "WARNING: --ignore-eos is set with --multi-turn. The server may not \
-                     respect output length limits, causing unbounded context growth."
+                tracing::warn!(
+                    ignore_eos,
+                    multi_turn = true,
+                    "output length limits may be ignored, causing unbounded context growth"
                );
            }

@@ -128,8 +128,10 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        crate::tokenizer::load_tokenizer("gpt2", false, None)
-            .expect("gpt2 built-in tiktoken should always load without network")
+        TokenizerKind::Tiktoken(
+            crate::tiktoken::load_builtin_tiktoken("gpt2")
+                .expect("gpt2 built-in tiktoken should always load without network"),
+        )
    }

    #[test]
@@ -8,6 +8,7 @@ use rand::seq::SliceRandom;
 use rand::{Rng, SeedableRng};

 use super::SampleRequest;
+use super::progress::RowDownloadReporter;
 use crate::error::{BenchError, Result};
 use crate::tokenizer::TokenizerKind;

@@ -50,18 +51,19 @@ enum ColumnFormat {

 /// Make a GET request with retry logic (3 retries with exponential backoff).
 /// Returns the parsed JSON response.
-fn get_with_retry(
-    client: &reqwest::blocking::Client,
+async fn get_with_retry(
+    client: &reqwest::Client,
    url: &str,
    label: &str,
 ) -> Result<serde_json::Value> {
    let max_retries = 3;
    for attempt in 0..=max_retries {
-        let resp = match client.get(url).send() {
+        let resp = match client.get(url).send().await {
            Ok(r) => r,
            Err(e) => {
                if attempt < max_retries {
-                    std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
+                    tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)))
+                        .await;
                    continue;
                }
                return Err(BenchError::Config(format!(
@@ -80,7 +82,7 @@ fn get_with_retry(
        }

        if status.is_server_error() && attempt < max_retries {
-            std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
+            tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1))).await;
            continue;
        }

@@ -92,6 +94,7 @@ fn get_with_retry(

        let data: serde_json::Value = resp
            .json()
+            .await
            .map_err(|e| BenchError::Config(format!("Failed to parse {label} response: {e}")))?;
        return Ok(data);
    }
@@ -105,7 +108,7 @@ fn get_with_retry(
 /// If both `subset` and `split` are provided, the `/info` call is skipped as an optimization.
 /// Paginated download fetches rows in pages of 100 until `num_rows_needed` are collected
 /// or the dataset is exhausted.
-pub fn download_hf_dataset(
+pub async fn download_hf_dataset(
    dataset: &str,
    subset: Option<&str>,
    split: Option<&str>,
@@ -115,7 +118,7 @@ pub fn download_hf_dataset(
        url::form_urlencoded::byte_serialize(dataset.as_bytes()).collect();

    let mut client_builder =
-        reqwest::blocking::Client::builder().timeout(std::time::Duration::from_secs(120));
+        reqwest::Client::builder().timeout(std::time::Duration::from_secs(120));

    // Add HF_TOKEN auth header if available
    if let Ok(token) = std::env::var("HF_TOKEN") {
@@ -138,7 +141,7 @@ pub fn download_hf_dataset(
        // Call /info to discover available configs and splits
        let info_url =
            format!("https://datasets-server.huggingface.co/info?dataset={encoded_dataset}");
-        let info = get_with_retry(&client, &info_url, "HF dataset /info")?;
+        let info = get_with_retry(&client, &info_url, "HF dataset /info").await?;

        let dataset_info =
            info.get("dataset_info").and_then(|d| d.as_object()).ok_or_else(|| {
@@ -201,7 +204,12 @@ pub fn download_hf_dataset(
        (resolved_config, resolved_split)
    };

-    println!("HF dataset: {dataset} (config={resolved_config}, split={resolved_split})");
+    tracing::info!(
+        dataset,
+        config = resolved_config,
+        split = resolved_split,
+        "resolved Hugging Face dataset"
+    );

    // Check cache
    let dir = cache_dir();
@@ -215,11 +223,16 @@ pub fn download_hf_dataset(

    if cache_path.exists() {
        let path_str = cache_path.to_string_lossy().to_string();
-        println!("HF dataset cached: {path_str}");
+        tracing::info!(dataset, path = %path_str, "using cached Hugging Face dataset");
        return Ok((path_str, resolved_config, resolved_split));
    }

-    println!("Downloading HF dataset '{dataset}' from datasets-server...");
+    tracing::info!(
+        dataset,
+        config = resolved_config,
+        split = resolved_split,
+        "downloading Hugging Face dataset"
+    );

    let encoded_config: String =
        url::form_urlencoded::byte_serialize(resolved_config.as_bytes()).collect();
@@ -229,6 +242,7 @@ pub fn download_hf_dataset(
    let mut all_rows: Vec<serde_json::Value> = Vec::new();
    let mut offset = 0usize;
    let page_size = 100usize;
+    let mut progress = RowDownloadReporter::new();

    loop {
        let url = format!(
@@ -240,7 +254,7 @@ pub fn download_hf_dataset(
             &length={page_size}"
        );

-        let data = get_with_retry(&client, &url, "HF dataset /rows")?;
+        let data = get_with_retry(&client, &url, "HF dataset /rows").await?;

        let rows = data["rows"]
            .as_array()
@@ -260,14 +274,14 @@ pub fn download_hf_dataset(
        offset += fetched;

        let total = data["num_rows_total"].as_u64().unwrap_or(0);
-        eprint!("\r  Fetched {offset}/{total} rows...");
+        progress.update(offset, total);

        // Stop if we have enough rows or reached end of dataset
        if all_rows.len() >= num_rows_needed || fetched < page_size {
            break;
        }
    }
-    eprintln!(); // newline after progress
+    progress.finish();

    if all_rows.is_empty() {
        return Err(BenchError::Config(format!(
@@ -280,7 +294,12 @@ pub fn download_hf_dataset(
    std::fs::write(&cache_path, &json_str)?;

    let path_str = cache_path.to_string_lossy().to_string();
-    println!("HF dataset: {} rows saved to {path_str}", all_rows.len());
+    tracing::info!(
+        dataset,
+        rows = all_rows.len(),
+        path = %path_str,
+        "saved Hugging Face dataset"
+    );
    Ok((path_str, resolved_config, resolved_split))
 }

@@ -483,21 +502,31 @@ pub fn load_hf_dataset(
    // Detect column format from first row
    let format = detect_column_format(&entries[0], text_column_override)?;

-    // Print detected format
    match &format {
-        ColumnFormat::Chat(col) => println!("HF dataset: detected chat column '{col}'"),
+        ColumnFormat::Chat(col) => {
+            tracing::info!(
+                format = "chat",
+                column = col,
+                "detected Hugging Face dataset format"
+            );
+        }
        ColumnFormat::Text {
            prompt_col,
            output_col,
        } => {
-            let out_msg = output_col.as_deref().unwrap_or("none");
-            println!("HF dataset: detected text column '{prompt_col}', output column: {out_msg}");
+            tracing::info!(
+                format = "text",
+                prompt_column = prompt_col,
+                output_column = output_col.as_deref().unwrap_or("none"),
+                "detected Hugging Face dataset format"
+            );
        }
        ColumnFormat::Combined { cols, output_col } => {
-            let out_msg = output_col.as_deref().unwrap_or("none");
-            println!(
-                "HF dataset: detected combined columns {:?}, output column: {out_msg}",
-                cols
+            tracing::info!(
+                format = "combined",
+                prompt_columns = ?cols,
+                output_column = output_col.as_deref().unwrap_or("none"),
+                "detected Hugging Face dataset format"
            );
        }
    }
@@ -608,9 +637,10 @@ pub fn load_hf_dataset(
                if len == 0 { 128 } else { len }
            } else {
                if !warned_no_output {
-                    eprintln!(
-                        "WARNING: No output column detected and --hf-output-len not set. \
-                         Using default output length of 128 tokens."
+                    tracing::warn!(
+                        path = dataset_path,
+                        default_output_tokens = 128,
+                        "no dataset output column or --hf-output-len; using default output length"
                    );
                    warned_no_output = true;
                }
@@ -618,9 +648,10 @@ pub fn load_hf_dataset(
            }
        } else {
            if !warned_no_output {
-                eprintln!(
-                    "WARNING: No output column detected and --hf-output-len not set. \
-                     Using default output length of 128 tokens."
+                tracing::warn!(
+                    path = dataset_path,
+                    default_output_tokens = 128,
+                    "no dataset output column or --hf-output-len; using default output length"
                );
                warned_no_output = true;
            }
@@ -640,9 +671,11 @@ pub fn load_hf_dataset(
    // Oversample if needed
    if samples.len() < num_requests {
        if no_oversample {
-            println!(
-                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
-                samples.len()
+            tracing::info!(
+                dataset = "hf",
+                samples = samples.len(),
+                requested = num_requests,
+                "skipping dataset oversampling"
            );
        } else if !samples.is_empty() {
            let original_len = samples.len();
@@ -652,9 +685,11 @@ pub fn load_hf_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            println!(
-                "Oversampled HF dataset from {original_len} to {} total samples.",
-                samples.len()
+            tracing::info!(
+                dataset = "hf",
+                original_samples = original_len,
+                samples = samples.len(),
+                "oversampled dataset"
            );
        }
    }
@@ -1002,8 +1037,10 @@ mod tests {

    /// Build a gpt2 tokenizer using built-in tiktoken encoding (no network required).
    fn builtin_tokenizer() -> crate::tokenizer::TokenizerKind {
-        crate::tokenizer::load_tokenizer("gpt2", false, None)
-            .expect("gpt2 built-in tiktoken should always load without network")
+        crate::tokenizer::TokenizerKind::Tiktoken(
+            crate::tiktoken::load_builtin_tiktoken("gpt2")
+                .expect("gpt2 built-in tiktoken should always load without network"),
+        )
    }

    /// Write JSON data to a unique temp file and return the path string.
@@ -5,6 +5,7 @@ pub mod custom;
 pub mod hf_dataset;
 pub mod multi_turn;
 pub mod prefix_repetition;
+mod progress;
 pub mod random;
 pub mod random_mm;
 pub mod random_rerank;
@@ -90,9 +91,10 @@ pub fn oversample_requests(
        return;
    }
    if no_oversample {
-        println!(
-            "Skipping oversampling. Total samples: {} (requested: {num_requests})",
-            requests.len()
+        tracing::info!(
+            samples = requests.len(),
+            requested = num_requests,
+            "skipping dataset oversampling"
        );
        return;
    }
@@ -103,9 +105,10 @@ pub fn oversample_requests(
        req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
        requests.push(req);
    }
-    println!(
-        "Oversampled requests from {original_len} to {} total samples.",
-        requests.len()
+    tracing::info!(
+        original_samples = original_len,
+        samples = requests.len(),
+        "oversampled dataset"
    );
 }

@@ -445,9 +445,10 @@ pub fn load_sharegpt_multi_turn(
            conv.conversation_id = format!("{request_id_prefix}conv-{}", original_len + i);
            conversations.push(conv);
        }
-        println!(
-            "Oversampled multi-turn conversations from {original_len} to {} total.",
-            conversations.len()
+        tracing::info!(
+            original_conversations = original_len,
+            conversations = conversations.len(),
+            "oversampled multi-turn conversations"
        );
    }

@@ -525,10 +526,12 @@ mod tests {
        len
    }

-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_prefix_sharing_structure() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();
+    async fn test_prefix_sharing_structure() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
+            .await
+            .unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 5,
@@ -610,10 +613,12 @@ mod tests {
        println!("All prefix sharing checks passed!");
    }

-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_per_turn_input_len_default_mode() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();
+    async fn test_per_turn_input_len_default_mode() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
+            .await
+            .unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 4,
@@ -650,10 +655,12 @@ mod tests {
        println!("per_turn_input_len default-mode checks passed!");
    }

-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_variable_turns_range() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();
+    async fn test_variable_turns_range() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
+            .await
+            .unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 50,
@@ -684,10 +691,12 @@ mod tests {
        println!("variable_turns_range checks passed! counts: {distinct_counts:?}");
    }

-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_variable_turns_fixed() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();
+    async fn test_variable_turns_fixed() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
+            .await
+            .unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 10,
@@ -709,10 +718,12 @@ mod tests {
        println!("variable_turns_fixed checks passed!");
    }

-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_per_turn_input_len_prefix_sharing() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();
+    async fn test_per_turn_input_len_prefix_sharing() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
+            .await
+            .unwrap();

        // Turn 0 input_len=1000, turns 1+ per_turn_input_len=600
        // global_len ≈ 100 (10%), conv_len ≈ 800 (80%), unique ≈ 100
@@ -41,11 +41,13 @@ pub fn generate_prefix_repetition_dataset(
    }
    let total = prompts_per_prefix * num_prefixes;
    if total != num_requests {
-        println!(
-            "prefix_repetition: generating {total} requests \
-             ({num_prefixes} prefixes x {prompts_per_prefix} prompts each; \
-             {} dropped to divide evenly)",
-            num_requests - total
+        tracing::info!(
+            requested = num_requests,
+            generated = total,
+            prefixes = num_prefixes,
+            prompts_per_prefix,
+            dropped = num_requests - total,
+            "adjusted prefix-repetition request count"
        );
    }

@@ -109,8 +111,10 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        crate::tokenizer::load_tokenizer("gpt2", false, None)
-            .expect("gpt2 built-in tiktoken should always load without network")
+        TokenizerKind::Tiktoken(
+            crate::tiktoken::load_builtin_tiktoken("gpt2")
+                .expect("gpt2 built-in tiktoken should always load without network"),
+        )
    }

    #[test]
@@ -0,0 +1,77 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use std::time::{Duration, Instant};
+
+use indicatif::{ProgressBar, ProgressStyle};
+
+const REPORT_INTERVAL: Duration = Duration::from_secs(10);
+
+/// Reports row download progress to an interactive progress bar, or through
+/// periodic tracing events when the progress bar is hidden on a non-TTY.
+pub(super) struct RowDownloadReporter {
+    progress: ProgressBar,
+    next_report: Instant,
+}
+
+impl RowDownloadReporter {
+    /// Creates a reporter that emits non-TTY updates every 10 seconds.
+    pub fn new() -> Self {
+        let progress = ProgressBar::new(0);
+        progress.set_style(
+            ProgressStyle::with_template(
+                "{spinner:.green} Fetching rows [{bar:30.cyan/blue}] {pos}/{len}",
+            )
+            .unwrap()
+            .progress_chars("#>-"),
+        );
+        Self {
+            progress,
+            next_report: Instant::now() + REPORT_INTERVAL,
+        }
+    }
+
+    /// Updates the current row count and reports progress when due.
+    pub fn update(&mut self, rows: usize, total: u64) {
+        let rows = rows as u64;
+        let total = total.max(rows);
+        self.progress.set_length(total);
+        self.progress.set_position(rows);
+
+        if self.should_report(Instant::now()) {
+            tracing::info!(rows, total, "fetching dataset rows");
+        }
+    }
+
+    /// Clears the interactive progress bar after the download completes.
+    pub fn finish(self) {
+        self.progress.finish_and_clear();
+    }
+
+    fn should_report(&mut self, now: Instant) -> bool {
+        if !self.progress.is_hidden() || now < self.next_report {
+            return false;
+        }
+        self.next_report = now + REPORT_INTERVAL;
+        true
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn hidden_reporter_uses_ten_second_deadline() {
+        let start = Instant::now();
+        let mut reporter = RowDownloadReporter {
+            progress: ProgressBar::hidden(),
+            next_report: start + REPORT_INTERVAL,
+        };
+
+        assert!(!reporter.should_report(start + Duration::from_secs(9)));
+        assert!(reporter.should_report(start + Duration::from_secs(10)));
+        assert!(!reporter.should_report(start + Duration::from_secs(19)));
+        assert!(reporter.should_report(start + Duration::from_secs(20)));
+    }
+}
@@ -49,9 +49,12 @@ pub fn generate_random_dataset(
    let (input_low, input_high) = range_ratio.input_bounds(real_input_len);
    let (output_low, output_high) = range_ratio.output_bounds(output_len);
    if !range_ratio.is_fixed() {
-        println!(
-            "Sampling input_len from [{input_low}, {input_high}] and \
-             output_len from [{output_low}, {output_high}]"
+        tracing::info!(
+            input_low,
+            input_high,
+            output_low,
+            output_high,
+            "sampling random request lengths"
        );
    }

@@ -305,7 +308,8 @@ mod tests {
    #[test]
    #[ignore]
    fn test_generate_random_dataset_token_ids() {
-        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
+        let tokenizer =
+            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
        let requests = generate_random_dataset(
            &tokenizer,
            10,  // num_requests
@@ -337,7 +341,8 @@ mod tests {
    #[test]
    #[ignore]
    fn test_generate_random_dataset_text() {
-        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
+        let tokenizer =
+            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
        let requests = generate_random_dataset(
            &tokenizer,
            10,  // num_requests
@@ -371,7 +376,8 @@ mod tests {
    #[test]
    #[ignore]
    fn test_token_length_exact_local() {
-        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
+        let tokenizer =
+            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
        let target_len = 512;
        let requests = generate_random_dataset(
            &tokenizer,
@@ -405,11 +411,11 @@ mod tests {
    }

    /// Test that tiktoken tokenizer produces exact target token lengths (token ID mode).
-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_token_length_exact_tiktoken() {
+    async fn test_token_length_exact_tiktoken() {
        // Use Qwen2.5 which has a tiktoken-format tokenizer
-        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None);
+        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None).await;
        let tokenizer = match tokenizer {
            Ok(t) => t,
            Err(e) => {
@@ -453,10 +459,10 @@ mod tests {

    /// Test encode/decode roundtrip stability for tiktoken.
    /// After one decode→encode cycle with UTF-8-safe tokens, length must not drift.
-    #[test]
+    #[tokio::test]
    #[ignore]
-    fn test_tiktoken_roundtrip_stability() {
-        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None);
+    async fn test_tiktoken_roundtrip_stability() {
+        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None).await;
        let tokenizer = match tokenizer {
            Ok(t) => t,
            Err(e) => {
@@ -139,8 +139,10 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        crate::tokenizer::load_tokenizer("gpt2", false, None)
-            .expect("gpt2 built-in tiktoken should always load without network")
+        TokenizerKind::Tiktoken(
+            crate::tiktoken::load_builtin_tiktoken("gpt2")
+                .expect("gpt2 built-in tiktoken should always load without network"),
+        )
    }

    fn fixed_ratio() -> RangeRatio {
@@ -22,18 +22,21 @@ const DEFAULT_SHAREGPT_FILE: &str = "ShareGPT_V3_unfiltered_cleaned_split.json";

 /// Download the default ShareGPT dataset from HuggingFace Hub.
 /// Uses hf-hub's built-in cache — subsequent calls return the cached path instantly.
-pub fn download_sharegpt_dataset() -> Result<String> {
-    println!(
-        "Downloading ShareGPT dataset from {DEFAULT_SHAREGPT_REPO}/{DEFAULT_SHAREGPT_FILE} ..."
+pub async fn download_sharegpt_dataset() -> Result<String> {
+    tracing::info!(
+        repository = DEFAULT_SHAREGPT_REPO,
+        file = DEFAULT_SHAREGPT_FILE,
+        "downloading ShareGPT dataset"
    );
-    let repo = crate::hub::HubRepo::dataset(DEFAULT_SHAREGPT_REPO.to_string());
-    let path = repo.get(DEFAULT_SHAREGPT_FILE).map_err(|e| {
+    let repo = crate::hub::HubRepo::dataset(DEFAULT_SHAREGPT_REPO.to_string())
+        .map_err(BenchError::Config)?;
+    let path = repo.get(DEFAULT_SHAREGPT_FILE).await.map_err(|e| {
        BenchError::Config(format!(
            "Failed to download ShareGPT dataset from '{DEFAULT_SHAREGPT_REPO}': {e}"
        ))
    })?;
    let path_str = path.to_string_lossy().to_string();
-    println!("ShareGPT dataset ready: {path_str}");
+    tracing::info!(dataset = "sharegpt", path = %path_str, "dataset is ready");
    Ok(path_str)
 }

@@ -135,9 +138,11 @@ pub fn load_sharegpt_dataset(
    // Oversample if dataset is smaller than requested
    if samples.len() < num_requests {
        if no_oversample {
-            println!(
-                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
-                samples.len()
+            tracing::info!(
+                dataset = "sharegpt",
+                samples = samples.len(),
+                requested = num_requests,
+                "skipping dataset oversampling"
            );
        } else if !samples.is_empty() {
            let needed = num_requests - samples.len();
@@ -147,9 +152,11 @@ pub fn load_sharegpt_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            println!(
-                "Oversampled requests from {original_len} to {} total samples.",
-                samples.len()
+            tracing::info!(
+                dataset = "sharegpt",
+                original_samples = original_len,
+                samples = samples.len(),
+                "oversampled dataset"
            );
        }
    }
@@ -8,6 +8,7 @@ use rand::seq::SliceRandom;
 use rand::{Rng, SeedableRng};

 use super::SampleRequest;
+use super::progress::RowDownloadReporter;
 use crate::cli::SpeedBenchConfig;
 use crate::error::{BenchError, Result};
 use crate::tokenizer::TokenizerKind;
@@ -25,7 +26,7 @@ fn cache_dir() -> std::path::PathBuf {

 /// Download SPEED-Bench dataset from HuggingFace datasets-server API.
 /// Results are cached as JSON locally for subsequent runs.
-pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
+pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    let config_name = config.as_str();

    let dir = cache_dir();
@@ -35,13 +36,13 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    // Return cached file if it exists
    if cache_path.exists() {
        let path_str = cache_path.to_string_lossy().to_string();
-        println!("SPEED-Bench ({config_name}) cached: {path_str}");
+        tracing::info!(config = config_name, path = %path_str, "using cached SPEED-Bench dataset");
        return Ok(path_str);
    }

-    println!("Downloading SPEED-Bench ({config_name}) from HuggingFace datasets-server...");
+    tracing::info!(config = config_name, "downloading SPEED-Bench dataset");

-    let client = reqwest::blocking::Client::builder()
+    let client = reqwest::Client::builder()
        .timeout(std::time::Duration::from_secs(120))
        .build()
        .map_err(|e| BenchError::Config(format!("Failed to build HTTP client: {e}")))?;
@@ -49,6 +50,7 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    let mut all_rows: Vec<serde_json::Value> = Vec::new();
    let mut offset = 0usize;
    let page_size = 100usize;
+    let mut progress = RowDownloadReporter::new();

    loop {
        let url = format!(
@@ -64,13 +66,14 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
        let max_retries = 3;
        let mut data: Option<serde_json::Value> = None;
        for attempt in 0..=max_retries {
-            let resp = match client.get(&url).send() {
+            let resp = match client.get(&url).send().await {
                Ok(r) => r,
                Err(e) => {
                    if attempt < max_retries {
-                        std::thread::sleep(std::time::Duration::from_secs(
+                        tokio::time::sleep(std::time::Duration::from_secs(
                            2 * (attempt as u64 + 1),
-                        ));
+                        ))
+                        .await;
                        continue;
                    }
                    return Err(BenchError::Config(format!(
@@ -80,7 +83,7 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
            };

            if resp.status().is_server_error() && attempt < max_retries {
-                std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
+                tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1))).await;
                continue;
            }

@@ -91,7 +94,7 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
                )));
            }

-            data = Some(resp.json().map_err(|e| {
+            data = Some(resp.json().await.map_err(|e| {
                BenchError::Config(format!("Failed to parse SPEED-Bench API response: {e}"))
            })?);
            break;
@@ -116,15 +119,14 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
        let fetched = rows.len();
        offset += fetched;

-        // Print progress
        let total = data["num_rows_total"].as_u64().unwrap_or(0);
-        eprint!("\r  Fetched {offset}/{total} rows...");
+        progress.update(offset, total);

        if fetched < page_size {
            break;
        }
    }
-    eprintln!(); // newline after progress
+    progress.finish();

    if all_rows.is_empty() {
        return Err(BenchError::Config(
@@ -137,9 +139,11 @@ pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    std::fs::write(&cache_path, &json_str)?;

    let path_str = cache_path.to_string_lossy().to_string();
-    println!(
-        "SPEED-Bench ({config_name}): {} rows saved to {path_str}",
-        all_rows.len()
+    tracing::info!(
+        config = config_name,
+        rows = all_rows.len(),
+        path = %path_str,
+        "saved SPEED-Bench dataset"
    );
    Ok(path_str)
 }
@@ -263,9 +267,11 @@ pub fn load_speed_bench_dataset(
    // Oversample if needed
    if samples.len() < num_requests {
        if no_oversample {
-            println!(
-                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
-                samples.len()
+            tracing::info!(
+                dataset = "speed-bench",
+                samples = samples.len(),
+                requested = num_requests,
+                "skipping dataset oversampling"
            );
        } else if !samples.is_empty() {
            let original_len = samples.len();
@@ -275,9 +281,11 @@ pub fn load_speed_bench_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            println!(
-                "Oversampled SPEED-Bench from {original_len} to {} total samples.",
-                samples.len()
+            tracing::info!(
+                dataset = "speed-bench",
+                original_samples = original_len,
+                samples = samples.len(),
+                "oversampled dataset"
            );
        }
    }
@@ -288,7 +296,6 @@ pub fn load_speed_bench_dataset(
        ));
    }

-    // Print category distribution
    let mut cat_counts: std::collections::HashMap<&str, usize> = std::collections::HashMap::new();
    for entry in &filtered[..filtered.len().min(samples.len())] {
        let cat = entry.get("category").and_then(|c| c.as_str()).unwrap_or("unknown");
@@ -297,7 +304,7 @@ pub fn load_speed_bench_dataset(
    let mut cats: Vec<_> = cat_counts.into_iter().collect();
    cats.sort_by_key(|b| std::cmp::Reverse(b.1));
    let cat_str: Vec<String> = cats.iter().map(|(k, v)| format!("{k}:{v}")).collect();
-    println!("SPEED-Bench categories: {}", cat_str.join(", "));
+    tracing::info!(categories = %cat_str.join(", "), "computed SPEED-Bench category distribution");

    Ok(samples)
 }
@@ -1,54 +1,39 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-//! Sync facade over the async `hf_hub` API.
-//!
-//! The workspace bans rustls (`rust/deny.toml`), but hf-hub's sync `ureq`
-//! backend unconditionally pulls ureq's default rustls feature. So we use the
-//! reqwest/native-tls tokio API instead, and bridge blocking callers (dataset
-//! loaders, tokenizer fallback in rayon threads) by running each download on a
-//! dedicated thread with its own single-threaded runtime.
-
 use std::path::PathBuf;

+use hf_hub::Repo;
+use hf_hub::api::tokio::{ApiBuilder, ApiRepo};
+
 /// A handle to a HuggingFace Hub repo, downloading via hf-hub's on-disk cache.
 pub struct HubRepo {
-    repo: hf_hub::Repo,
+    repo: ApiRepo,
 }

 impl HubRepo {
-    pub fn model(model_id: String) -> Self {
-        Self {
-            repo: hf_hub::Repo::model(model_id),
-        }
+    pub fn model(model_id: String) -> Result<Self, String> {
+        Self::new(Repo::model(model_id))
    }

-    pub fn dataset(repo_id: String) -> Self {
-        Self {
-            repo: hf_hub::Repo::dataset(repo_id),
+    pub fn dataset(repo_id: String) -> Result<Self, String> {
+        Self::new(Repo::dataset(repo_id))
+    }
+
+    fn new(repo: Repo) -> Result<Self, String> {
+        let mut builder = ApiBuilder::from_env();
+        if let Ok(token) = std::env::var("HF_TOKEN") {
+            builder = builder.with_token(Some(token));
        }
+        let api = builder.build().map_err(|e| format!("Failed to init HF API: {e}"))?;
+        Ok(Self {
+            repo: api.repo(repo),
+        })
    }

    /// Download (or fetch from cache) a single file from the repo.
    /// Auth is handled by hf-hub via HF_TOKEN / the cached login token.
-    pub fn get(&self, filename: &str) -> Result<PathBuf, String> {
-        let repo = self.repo.clone();
-        let filename = filename.to_string();
-        std::thread::spawn(move || {
-            let rt = tokio::runtime::Builder::new_current_thread()
-                .enable_all()
-                .build()
-                .map_err(|e| format!("Failed to build download runtime: {e}"))?;
-            rt.block_on(async move {
-                let mut builder = hf_hub::api::tokio::ApiBuilder::from_env();
-                if let Ok(token) = std::env::var("HF_TOKEN") {
-                    builder = builder.with_token(Some(token));
-                }
-                let api = builder.build().map_err(|e| format!("Failed to init HF API: {e}"))?;
-                api.repo(repo).get(&filename).await.map_err(|e| format!("{e}"))
-            })
-        })
-        .join()
-        .map_err(|_| "HF Hub download thread panicked".to_string())?
+    pub async fn get(&self, filename: &str) -> Result<PathBuf, String> {
+        self.repo.get(filename).await.map_err(|e| format!("{e}"))
    }
 }
@@ -33,7 +33,7 @@ pub fn prepare_process() {
    if let Ok(new) = rlimit::increase_nofile_limit(u64::MAX)
        && new > 1024
    {
-        eprintln!("Open-file limit: {new}");
+        tracing::info!(soft_limit = new, "raised open-file limit");
    }
 }

@@ -19,7 +19,19 @@ struct Cli {
    args: vllm_bench::BenchServeArgs,
 }

+// TODO: unify the tracing subscriber used by different binaries.
+fn init_tracing() {
+    let filter = tracing_subscriber::EnvFilter::try_from_default_env()
+        .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info"));
+    let _ = tracing_subscriber::fmt()
+        .with_env_filter(filter)
+        .with_writer(std::io::stderr)
+        .try_init();
+}
+
 fn main() -> anyhow::Result<()> {
+    init_tracing();
+
    let cli = Cli::parse();
    vllm_bench::prepare_process();

@@ -7,6 +7,22 @@ use crate::datasets::SampleRequest;
 use crate::metrics::{BenchmarkMetrics, MultiTurnMetrics};
 use crate::multi_turn::ConversationOutput;

+fn log_failed_requests(outputs: &[RequestFuncOutput]) {
+    let failed_outputs: Vec<_> = outputs.iter().filter(|output| !output.success).collect();
+    if failed_outputs.is_empty() {
+        return;
+    }
+
+    tracing::warn!(
+        failed_requests = failed_outputs.len(),
+        displayed_errors = failed_outputs.len().min(10),
+        "benchmark requests failed"
+    );
+    for (index, output) in failed_outputs.into_iter().take(10).enumerate() {
+        tracing::warn!(index, error = %output.error, "benchmark request failed");
+    }
+}
+
 /// Calculate benchmark metrics from request outputs.
 ///
 /// Mirrors Python's `calculate_metrics()` from serve.py:392-599.
@@ -63,14 +79,7 @@ pub fn calculate_metrics(

    let failed = outputs.len() - completed;

-    // Print failed request errors (capped to 10)
-    let failed_outputs: Vec<&RequestFuncOutput> = outputs.iter().filter(|o| !o.success).collect();
-    if !failed_outputs.is_empty() {
-        eprintln!("Failed requests during benchmark run detected (capping to 10):");
-        for (i, err) in failed_outputs.iter().take(10).enumerate() {
-            eprintln!("Error {i}: {}", err.error);
-        }
-    }
+    log_failed_requests(outputs);

    // Calculate max output tokens per second and max concurrent requests
    let mut max_output_tokens_per_s = 0.0_f64;
@@ -295,14 +304,7 @@ pub fn calculate_embedding_metrics(

    let failed = outputs.len() - completed;

-    // Print failed request errors (capped to 10)
-    let failed_outputs: Vec<&RequestFuncOutput> = outputs.iter().filter(|o| !o.success).collect();
-    if !failed_outputs.is_empty() {
-        eprintln!("Failed requests during benchmark run detected (capping to 10):");
-        for (i, err) in failed_outputs.iter().take(10).enumerate() {
-            eprintln!("Error {i}: {}", err.error);
-        }
-    }
+    log_failed_requests(outputs);

    // Compute peak concurrent requests from start_time + latency windows
    let successful_outputs: Vec<&RequestFuncOutput> =
@@ -6,6 +6,7 @@ use std::sync::Arc;
 use std::time::Instant;

 use indicatif::{ProgressBar, ProgressStyle};
+use thiserror_ext::AsReport as _;
 use tokio::sync::Semaphore;

 use crate::backends::{Backend, RequestFuncInput, RequestFuncOutput, get_backend};
@@ -72,9 +73,13 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    let (model_id, model_name) = if let Some(ref m) = config.model {
        (m.clone(), config.model_name.clone())
    } else {
-        println!("Model not specified, fetching first model from server...");
+        tracing::info!(base_url = %config.base_url, "fetching first model from server");
        let (name, id) = get_first_model(&config.base_url, &client, &config.extra_headers).await?;
-        println!("First model name: {name}, first model id: {id}");
+        tracing::info!(
+            model_name = name,
+            model_id = id,
+            "selected first model from server"
+        );
        (id, Some(name))
    };

@@ -83,15 +88,19 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        None
    } else {
        let tid = config.tokenizer_id.as_deref().unwrap_or(&model_id);
-        println!("Loading tokenizer: {tid}");
+        tracing::info!(tokenizer = tid, "loading tokenizer");
        let server_info = Some((config.base_url.as_str(), model_id.as_str()));
-        let t = crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info)?;
-        println!("Tokenizer loaded successfully.");
+        let t =
+            crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info).await?;
        Some(t)
    };

    // Generate/load conversations
-    println!("Generating multi-turn conversations...");
+    tracing::info!(
+        dataset = ?config.dataset_name,
+        conversations = config.num_prompts,
+        "generating multi-turn conversations"
+    );
    let gen_start = Instant::now();

    let mut conversations = match config.dataset_name {
@@ -131,7 +140,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
            let path = match config.dataset_path.as_deref() {
                Some(p) => p,
                None => {
-                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset()?;
+                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset().await?;
                    downloaded.as_str()
                }
            };
@@ -179,8 +188,11 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        let (filtered_conversations, filtered_turns) =
            filter_turns_by_max_model_len(&mut conversations, max_model_len, no_history);
        if filtered_turns > 0 || filtered_conversations > 0 {
-            println!(
-                "Filtered {filtered_turns} turn(s) and {filtered_conversations} conversation(s) above --max-model-len {max_model_len}."
+            tracing::info!(
+                filtered_turns,
+                filtered_conversations,
+                max_model_len,
+                "filtered conversations above maximum model length"
            );
        }
        if conversations.is_empty() {
@@ -192,11 +204,11 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json

    let gen_elapsed = gen_start.elapsed();
    let total_turns: usize = conversations.iter().map(|c| c.turns.len()).sum();
-    println!(
-        "Generated {} conversations ({} total turns) in {:.2}s",
-        conversations.len(),
+    tracing::info!(
+        conversations = conversations.len(),
        total_turns,
-        gen_elapsed.as_secs_f64()
+        elapsed_seconds = gen_elapsed.as_secs_f64(),
+        "generated multi-turn conversations"
    );

    // Log prefix sharing info
@@ -208,18 +220,15 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        let conv_tokens =
            (real_input_len as f64 * config.multi_turn_prefix_conversation_ratio).floor() as usize;
        let unique_tokens = real_input_len.saturating_sub(global_tokens + conv_tokens);
-        println!(
-            "User message prefix sharing: {:.0}% global ({} tokens), {:.0}% per-conversation ({} tokens), {:.0}% unique ({} tokens)",
-            config.multi_turn_prefix_global_ratio * 100.0,
+        tracing::info!(
+            global_ratio = config.multi_turn_prefix_global_ratio,
            global_tokens,
-            config.multi_turn_prefix_conversation_ratio * 100.0,
-            conv_tokens,
-            (1.0 - config.multi_turn_prefix_global_ratio
-                - config.multi_turn_prefix_conversation_ratio)
-                * 100.0,
+            conversation_ratio = config.multi_turn_prefix_conversation_ratio,
+            conversation_tokens = conv_tokens,
            unique_tokens,
+            history_accumulation = false,
+            "configured multi-turn prefix sharing"
        );
-        println!("No history accumulation: each turn sends fixed-length prompt only.");
    }

    if config.dry_run {
@@ -253,7 +262,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
            ..Default::default()
        };

-        println!("Starting initial single prompt test run...");
+        tracing::info!("starting initial single-prompt test run");
        let test_output = crate::ready_checker::wait_for_endpoint(
            config.backend,
            &client,
@@ -268,7 +277,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
                test_output.error
            )));
        }
-        println!("Initial test run completed.");
+        tracing::info!("initial single-prompt test run completed");
    }

    // For random datasets in multi-turn mode, auto-set min_tokens to enforce
@@ -283,9 +292,10 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
                "min_tokens".to_string(),
                serde_json::json!(config.random_output_len),
            );
-            println!(
-                "Auto-setting min_tokens={} for multi-turn random dataset (use --extra-body to override)",
-                config.random_output_len
+            tracing::info!(
+                min_tokens = config.random_output_len,
+                dataset = "random",
+                "set minimum output tokens for multi-turn dataset"
            );
        }
        Some(body)
@@ -297,7 +307,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    let spec_decode_before =
        fetch_spec_decode_metrics(&config.base_url, &client, &config.extra_headers).await;
    if spec_decode_before.is_some() {
-        println!("Speculative decoding detected, will collect metrics.");
+        tracing::info!("detected speculative decoding; collecting metrics");
    }

    // Start profiler if requested (immediate mode — no batch threshold)
@@ -330,10 +340,13 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    };

    // Main benchmark
-    println!("Starting multi-turn benchmark...");
-    println!("Conversations: {}", conversations.len());
-    println!("Concurrency: {concurrency}");
-    println!("Inter-turn delay: {} ms", config.multi_turn_delay_ms);
+    tracing::info!(
+        conversations = conversations.len(),
+        total_turns,
+        concurrency,
+        inter_turn_delay_ms = config.multi_turn_delay_ms,
+        "starting multi-turn benchmark"
+    );

    let max_turn_count = conversations.iter().map(|c| c.turns.len()).max().unwrap_or(0);

@@ -364,11 +377,12 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    );
    if let Some(modules) = config.lora_modules.as_ref() {
        let names: Vec<&str> = modules.iter().map(|s| s.as_ref()).collect();
-        println!(
-            "LoRA adapters ({}): {:?} [assignment={:?}, scope=conversation]",
-            modules.len(),
-            names,
-            config.lora_assignment
+        tracing::info!(
+            adapters = modules.len(),
+            names = ?names,
+            assignment = ?config.lora_assignment,
+            scope = "conversation",
+            "assigned LoRA adapters"
        );
    }

@@ -433,7 +447,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        match handle.await {
            Ok(output) => all_outputs.push(output),
            Err(e) => {
-                eprintln!("Conversation task panicked: {e}");
+                tracing::error!(error = %e.as_report(), "conversation task panicked");
            }
        }
    }
@@ -453,7 +467,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    if let Some((cancel_tx, task)) = profile_task {
        let _ = cancel_tx.send(());
        if let Err(e) = task.await {
-            eprintln!("WARNING: Profile background task failed: {e}");
+            tracing::error!(error = %e.as_report(), "profiler background task failed");
        }
    }

@@ -603,7 +603,7 @@ fn add_metric_stats(
 pub fn save_result(json: &Value, file_path: &str) -> Result<()> {
    let content = serde_json::to_string(json)?;
    std::fs::write(file_path, content)?;
-    println!("Results saved to {file_path}");
+    tracing::info!(path = file_path, "saved benchmark results");
    Ok(())
 }

@@ -618,7 +618,7 @@ pub fn append_result(json: &Value, file_path: &str) -> Result<()> {
        file.write_all(b"\n")?;
    }
    file.write_all(content.as_bytes())?;
-    println!("Results appended to {file_path}");
+    tracing::info!(path = file_path, "appended benchmark results");
    Ok(())
 }

@@ -23,7 +23,11 @@ pub async fn wait_for_endpoint(
    let backend = get_backend(backend)?;
    let deadline = Instant::now() + std::time::Duration::from_secs(timeout_seconds);

-    println!("Waiting for endpoint to become up in {timeout_seconds}s");
+    tracing::info!(
+        timeout_seconds,
+        retry_interval,
+        "waiting for endpoint readiness"
+    );

    let pb = ProgressBar::new(timeout_seconds);
    pb.set_style(
@@ -53,7 +57,9 @@ pub async fn wait_for_endpoint(
            Ok(output) => {
                let err = output.error.clone();
                let err_last_line = err.lines().last().unwrap_or(&err);
-                eprintln!("Endpoint is not ready. Error='{err_last_line}'");
+                pb.suspend(|| {
+                    tracing::warn!(error = err_last_line, "endpoint is not ready");
+                });
                last_error = err;
            }
            Err(e) => {
@@ -16,7 +16,7 @@ async fn reset_prefix_cache(base_url: &str) -> Result<()> {
        .await
        .map_err(|e| BenchError::Backend(format!("Failed to reset prefix cache: {e}")))?;
    if resp.status().is_success() {
-        println!("Prefix cache reset successfully.");
+        tracing::info!(url = %url, "reset prefix cache");
    } else {
        let status = resp.status();
        let body = resp.text().await.unwrap_or_default();
@@ -199,12 +199,17 @@ pub fn load_builtin_tiktoken(encoding: &str) -> Result<TiktokenTokenizer> {
        }
    };
    let bpe = bpe.map_err(|e| BenchError::Tokenizer(format!("Failed to load {encoding}: {e}")))?;
-    println!("Tokenizer: Built-in tiktoken {encoding} (vocab_size={vocab_size})");
+    tracing::info!(
+        encoding,
+        kind = "built-in-tiktoken",
+        vocab_size,
+        "loaded tokenizer"
+    );
    Ok(TiktokenTokenizer::from_builtin_bpe(bpe, vocab_size))
 }

 /// Try to load a tiktoken tokenizer from a local directory or HuggingFace model repo.
-pub fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
+pub async fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
    // Phase 1: If model_id is a local directory, look for tiktoken files there
    let local_dir = Path::new(model_id);
    if local_dir.is_dir() {
@@ -212,7 +217,7 @@ pub fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
    }

    // Phase 2: Fall back to HuggingFace Hub download
-    try_load_tiktoken_from_hf(model_id)
+    try_load_tiktoken_from_hf(model_id).await
 }

 /// Common tiktoken model filenames to search for.
@@ -247,25 +252,28 @@ fn try_load_tiktoken_from_dir(dir: &Path, model_id: &str) -> Result<TiktokenToke
 }

 /// Load a tiktoken tokenizer from a HuggingFace model repo.
-fn try_load_tiktoken_from_hf(model_id: &str) -> Result<TiktokenTokenizer> {
-    let repo = crate::hub::HubRepo::model(model_id.to_string());
+async fn try_load_tiktoken_from_hf(model_id: &str) -> Result<TiktokenTokenizer> {
+    let repo = crate::hub::HubRepo::model(model_id.to_string()).map_err(BenchError::Tokenizer)?;

-    let model_path = repo
-        .get("tiktoken.model")
-        .or_else(|_| repo.get("qwen.tiktoken"))
-        .or_else(|_| repo.get("vocab.tiktoken"))
-        .map_err(|_| {
-            BenchError::Tokenizer(format!("No tiktoken model file found for '{model_id}'"))
-        })?;
+    let mut model_path = None;
+    for filename in TIKTOKEN_MODEL_FILENAMES {
+        if let Ok(path) = repo.get(filename).await {
+            model_path = Some(path);
+            break;
+        }
+    }
+    let model_path = model_path.ok_or_else(|| {
+        BenchError::Tokenizer(format!("No tiktoken model file found for '{model_id}'"))
+    })?;

    let num_base_tokens = count_base_tokens(&model_path)?;

-    let config = match repo.get("tokenizer_config.json") {
+    let config = match repo.get("tokenizer_config.json").await {
        Ok(config_path) => read_tokenizer_config(&config_path),
        Err(_) => None,
    };

-    let pattern = extract_pat_str_from_repo(&repo);
+    let pattern = extract_pat_str_from_repo(&repo).await;

    build_tiktoken(model_id, &model_path, config, pattern, num_base_tokens)
 }
@@ -309,15 +317,16 @@ fn build_tiktoken(
        }
    }

-    println!(
-        "Loading tiktoken model for '{model_id}' (base={}, special={}, pat={})...",
-        num_base_tokens,
-        all_special_tokens.len(),
-        if pattern.is_some() {
+    tracing::info!(
+        model = model_id,
+        base_tokens = num_base_tokens,
+        special_tokens = all_special_tokens.len(),
+        pattern = if pattern.is_some() {
            "custom"
        } else {
            "default"
        },
+        "loading tiktoken model"
    );

    TiktokenTokenizer::from_file(
@@ -397,9 +406,12 @@ fn extract_pat_str_from_local_dir(dir: &Path) -> Option<String> {

 /// Try to download the Python tokenizer source file and extract pat_str via regex.
 /// Returns None if unavailable or unparsable.
-fn extract_pat_str_from_repo(repo: &crate::hub::HubRepo) -> Option<String> {
+async fn extract_pat_str_from_repo(repo: &crate::hub::HubRepo) -> Option<String> {
    // Try common Python tokenizer filenames
-    let py_path = repo.get("tokenization_kimi.py").or_else(|_| repo.get("tokenizer.py")).ok()?;
+    let py_path = match repo.get("tokenization_kimi.py").await {
+        Ok(path) => path,
+        Err(_) => repo.get("tokenizer.py").await.ok()?,
+    };

    let source = std::fs::read_to_string(&py_path).ok()?;

@@ -438,9 +450,9 @@ fn extract_pat_str_from_source(source: &str) -> Option<String> {

                if !fragments.is_empty() {
                    let pattern = fragments.join("|");
-                    println!(
-                        "Extracted pat_str from Python source: {} fragments",
-                        fragments.len()
+                    tracing::debug!(
+                        fragments = fragments.len(),
+                        "extracted tiktoken pattern from Python source"
                    );
                    return Some(pattern);
                }
@@ -2,8 +2,10 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use std::collections::HashSet;
+use std::future::Future;
 use std::path::Path;

+use thiserror_ext::AsReport as _;
 use tokenizers::Tokenizer;

 use crate::error::{BenchError, Result};
@@ -18,7 +20,8 @@ pub enum TokenizerKind {

 /// Server-side tokenizer using vLLM's /tokenize and /detokenize endpoints.
 pub struct ServerTokenizer {
-    client: reqwest::blocking::Client,
+    client: reqwest::Client,
+    runtime: tokio::runtime::Handle,
    tokenize_url: String,
    detokenize_url: String,
    model: String,
@@ -27,8 +30,8 @@ pub struct ServerTokenizer {

 impl ServerTokenizer {
    /// Create a new server tokenizer and verify connectivity.
-    pub fn new(base_url: &str, model: &str) -> Result<Self> {
-        let client = reqwest::blocking::Client::builder()
+    pub async fn new(base_url: &str, model: &str) -> Result<Self> {
+        let client = reqwest::Client::builder()
            .timeout(std::time::Duration::from_secs(30))
            .build()
            .map_err(|e| BenchError::Tokenizer(format!("Failed to build HTTP client: {e}")))?;
@@ -38,6 +41,7 @@ impl ServerTokenizer {

        let st = Self {
            client,
+            runtime: tokio::runtime::Handle::current(),
            tokenize_url,
            detokenize_url,
            model: model.to_string(),
@@ -45,7 +49,7 @@ impl ServerTokenizer {
        };

        // Probe the endpoint to verify it works and discover vocab size
-        let test_tokens = st.encode_inner("test")?;
+        let test_tokens = st.encode_async("test").await?;
        let max_id = test_tokens.iter().copied().max().unwrap_or(0);
        let estimated_vocab = (max_id * 2).max(131072);

@@ -56,6 +60,10 @@ impl ServerTokenizer {
    }

    fn encode_inner(&self, text: &str) -> Result<Vec<u32>> {
+        self.block_on(self.encode_async(text))
+    }
+
+    async fn encode_async(&self, text: &str) -> Result<Vec<u32>> {
        let payload = serde_json::json!({
            "model": self.model,
            "prompt": text,
@@ -66,6 +74,7 @@ impl ServerTokenizer {
            .post(&self.tokenize_url)
            .json(&payload)
            .send()
+            .await
            .map_err(|e| BenchError::Tokenizer(format!("Server tokenize failed: {e}")))?;

        if !resp.status().is_success() {
@@ -75,7 +84,7 @@ impl ServerTokenizer {
            )));
        }

-        let data: serde_json::Value = resp.json().map_err(|e| {
+        let data: serde_json::Value = resp.json().await.map_err(|e| {
            BenchError::Tokenizer(format!("Failed to parse tokenize response: {e}"))
        })?;

@@ -95,6 +104,10 @@ impl ServerTokenizer {
    }

    fn decode_inner(&self, ids: &[u32]) -> Result<String> {
+        self.block_on(self.decode_async(ids))
+    }
+
+    async fn decode_async(&self, ids: &[u32]) -> Result<String> {
        let payload = serde_json::json!({
            "model": self.model,
            "tokens": ids,
@@ -105,6 +118,7 @@ impl ServerTokenizer {
            .post(&self.detokenize_url)
            .json(&payload)
            .send()
+            .await
            .map_err(|e| BenchError::Tokenizer(format!("Server detokenize failed: {e}")))?;

        if !resp.status().is_success() {
@@ -114,7 +128,7 @@ impl ServerTokenizer {
            )));
        }

-        let data: serde_json::Value = resp.json().map_err(|e| {
+        let data: serde_json::Value = resp.json().await.map_err(|e| {
            BenchError::Tokenizer(format!("Failed to parse detokenize response: {e}"))
        })?;

@@ -123,6 +137,26 @@ impl ServerTokenizer {
            .map(|s| s.to_string())
            .ok_or_else(|| BenchError::Tokenizer("Missing 'prompt' in detokenize response".into()))
    }
+
+    fn block_on<T>(&self, future: impl Future<Output = Result<T>>) -> Result<T> {
+        if matches!(
+            self.runtime.runtime_flavor(),
+            tokio::runtime::RuntimeFlavor::CurrentThread
+        ) {
+            return Err(BenchError::Tokenizer(
+                "Server tokenizer fallback requires a multi-thread Tokio runtime".into(),
+            ));
+        }
+
+        // Sync tokenizer calls can come from a Tokio worker or a Rayon worker.
+        // Tokio workers must enter a blocking region before re-entering the runtime;
+        // Rayon workers can drive the future directly with the saved runtime handle.
+        if tokio::runtime::Handle::try_current().is_ok() {
+            tokio::task::block_in_place(|| self.runtime.block_on(future))
+        } else {
+            self.runtime.block_on(future)
+        }
+    }
 }

 // --- TokenizerKind methods ---
@@ -192,7 +226,7 @@ impl TokenizerKind {
 /// 3. Server-side /tokenize + /detokenize endpoints
 ///
 /// `server_info` is `Some((base_url, model))` to enable server-side fallback.
-pub fn load_tokenizer(
+pub async fn load_tokenizer(
    model_id: &str,
    _trust_remote_code: bool,
    server_info: Option<(&str, &str)>,
@@ -212,31 +246,48 @@ pub fn load_tokenizer(
    }

    // 1. Try local HuggingFace tokenizer (tokenizer.json)
-    match try_load_local(model_id) {
+    match try_load_local(model_id).await {
        Ok(tok) => {
-            println!("Tokenizer: Local (vocab_size={})", tok.get_vocab_size(true));
+            tracing::info!(
+                model = model_id,
+                kind = "local",
+                vocab_size = tok.get_vocab_size(true),
+                "loaded tokenizer"
+            );
            Ok(TokenizerKind::Local(Box::new(tok)))
        }
        Err(local_err) => {
            // 2. Try tiktoken format
-            println!("No tokenizer.json for '{model_id}', trying tiktoken format...");
-            match crate::tiktoken::try_load_tiktoken(model_id) {
+            tracing::info!(
+                model = model_id,
+                error = %local_err.as_report(),
+                "local tokenizer unavailable; trying tiktoken"
+            );
+            match crate::tiktoken::try_load_tiktoken(model_id).await {
                Ok(tok) => {
-                    println!("Tokenizer: Tiktoken (vocab_size={})", tok.vocab_size());
+                    tracing::info!(
+                        model = model_id,
+                        kind = "tiktoken",
+                        vocab_size = tok.vocab_size(),
+                        "loaded tokenizer"
+                    );
                    Ok(TokenizerKind::Tiktoken(tok))
                }
                Err(tiktoken_err) => {
                    // 3. Try server-side fallback
                    if let Some((base_url, model)) = server_info {
-                        println!(
-                            "Tiktoken also not available ({tiktoken_err}), \
-                             trying server-side tokenization..."
+                        tracing::info!(
+                            model = model_id,
+                            error = %tiktoken_err.as_report(),
+                            "tiktoken unavailable; trying server-side tokenization"
                        );
-                        match ServerTokenizer::new(base_url, model) {
+                        match ServerTokenizer::new(base_url, model).await {
                            Ok(srv) => {
-                                println!(
-                                    "Tokenizer: Server (vocab_size≈{})",
-                                    srv.cached_vocab_size
+                                tracing::info!(
+                                    model = model_id,
+                                    kind = "server",
+                                    vocab_size = srv.cached_vocab_size,
+                                    "loaded tokenizer"
                                );
                                return Ok(TokenizerKind::Server(srv));
                            }
@@ -264,7 +315,7 @@ pub fn load_tokenizer(
 }

 /// Try loading tokenizer.json from local path or HuggingFace Hub.
-fn try_load_local(model_id: &str) -> Result<Tokenizer> {
+async fn try_load_local(model_id: &str) -> Result<Tokenizer> {
    // 1. Try local directory with tokenizer.json
    let local_path = Path::new(model_id).join("tokenizer.json");
    if local_path.exists() {
@@ -290,11 +341,37 @@ fn try_load_local(model_id: &str) -> Result<Tokenizer> {
    }

    // 4. Download from HuggingFace Hub (hf-hub handles auth via HF_TOKEN / cached token)
-    let repo = crate::hub::HubRepo::model(model_id.to_string());
+    let repo = crate::hub::HubRepo::model(model_id.to_string()).map_err(BenchError::Tokenizer)?;
    let tokenizer_path = repo
        .get("tokenizer.json")
+        .await
        .map_err(|e| BenchError::Tokenizer(format!("No tokenizer.json for '{model_id}': {e}")))?;

    Tokenizer::from_file(&tokenizer_path)
        .map_err(|e| BenchError::Tokenizer(format!("Failed to load downloaded tokenizer: {e}")))
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
+    async fn test_server_tokenizer_sync_bridge() {
+        let tokenizer = std::sync::Arc::new(ServerTokenizer {
+            client: reqwest::Client::new(),
+            runtime: tokio::runtime::Handle::current(),
+            tokenize_url: String::new(),
+            detokenize_url: String::new(),
+            model: String::new(),
+            cached_vocab_size: 0,
+        });
+
+        assert_eq!(tokenizer.block_on(async { Ok(1) }).unwrap(), 1);
+
+        let (tx, rx) = tokio::sync::oneshot::channel();
+        rayon::spawn(move || {
+            let _ = tx.send(tokenizer.block_on(async { Ok(2) }));
+        });
+        assert_eq!(rx.await.unwrap().unwrap(), 2);
+    }
+}
@@ -54,6 +54,7 @@ mod stream;

 use vllm_engine_core_client::EngineCoreClient;
 use vllm_engine_core_client::protocol::dtype::ModelDtype;
+use vllm_engine_core_client::protocol::multimodal::MmFeatures;
 use vllm_engine_core_client::protocol::request::ReasoningParserKwargs;
 use vllm_llm::Llm;
 use vllm_text::{Prompt, TextLlm, TextRequest};
@@ -88,6 +89,92 @@ pub fn validate_parser_overrides(
    Ok(())
 }

+/// Chat request preparation shared by inference and render-only frontends.
+pub struct ChatRequestProcessor {
+    backend: DynChatBackend,
+    /// Effective model dtype reported by the engine.
+    /// Absent for text-only frontends without an engine handshake.
+    model_dtype: Option<ModelDtype>,
+}
+
+impl ChatRequestProcessor {
+    /// Create a processor with multimodal support using the effective model
+    /// dtype reported by the engine.
+    fn new(backend: DynChatBackend, model_dtype: ModelDtype) -> Self {
+        Self {
+            backend,
+            model_dtype: Some(model_dtype),
+        }
+    }
+
+    /// Create a render-only processor that rejects multimodal requests.
+    pub fn render_only(backend: DynChatBackend) -> Self {
+        Self {
+            backend,
+            model_dtype: None,
+        }
+    }
+
+    async fn finalize_rendered_prompt(
+        &self,
+        request: &ChatRequest,
+        rendered: RenderedPrompt,
+    ) -> Result<(Prompt, Option<MmFeatures>)> {
+        match self.model_dtype {
+            Some(model_dtype) => {
+                multimodal::finalize_rendered_prompt(
+                    request,
+                    rendered,
+                    self.backend.multimodal_model_info(),
+                    model_dtype,
+                )
+                .await
+            }
+            None if !request.has_multimodal() => Ok((rendered.prompt, None)),
+            None => Err(Error::UnsupportedMultimodalRenderer),
+        }
+    }
+
+    /// Prepare one chat request without submitting it to an engine.
+    pub async fn prepare(
+        &self,
+        mut request: ChatRequest,
+        options: NewChatOutputProcessorOptions<'_>,
+    ) -> Result<(TextRequest, DynChatOutputProcessor)> {
+        request.validate()?;
+
+        // Stamp before rendering so render and tokenize count toward TTFT/e2e.
+        let arrival_time = vllm_llm::current_unix_timestamp_secs();
+        let output_processor = self.backend.new_chat_output_processor(&mut request, options)?;
+        let rendered = self.backend.chat_renderer().render(&request)?;
+        let reasoning_parser_kwargs =
+            request
+                .sampling_params
+                .structured_outputs
+                .is_some()
+                .then(|| ReasoningParserKwargs {
+                    chat_template_kwargs: rendered.effective_template_kwargs.clone(),
+                });
+        let (prompt, mm_features) = self.finalize_rendered_prompt(&request, rendered).await?;
+        let text_request = TextRequest {
+            request_id: request.request_id,
+            prompt,
+            mm_features,
+            sampling_params: request.sampling_params,
+            decode_options: request.decode_options,
+            intermediate: request.intermediate,
+            priority: request.priority,
+            cache_salt: request.cache_salt,
+            add_special_tokens: request.add_special_tokens,
+            data_parallel_rank: request.data_parallel_rank,
+            reasoning_parser_kwargs,
+            lora_request: request.lora_request,
+            arrival_time: Some(arrival_time),
+        };
+        Ok((text_request, output_processor))
+    }
+}
+
 /// Structured chat facade above [`TextLlm`].
 ///
 /// This layer stays above raw text semantics: it takes care of chat-template
@@ -95,9 +182,7 @@ pub fn validate_parser_overrides(
 /// request semantics such as tool calls.
 pub struct ChatLlm {
    text: TextLlm,
-    backend: DynChatBackend,
-    /// Effective model dtype reported by the engine.
-    model_dtype: ModelDtype,
+    processor: ChatRequestProcessor,
    /// Tool-call parser selection.
    tool_call_parser: ParserSelection,
    /// Reasoning parser selection.
@@ -112,8 +197,7 @@ impl ChatLlm {

        Self {
            text,
-            backend,
-            model_dtype,
+            processor: ChatRequestProcessor::new(backend, model_dtype),
            tool_call_parser: ParserSelection::Auto,
            reasoning_parser: ParserSelection::Auto,
        }
@@ -140,7 +224,7 @@ impl ChatLlm {

    /// Override the effective model dtype used for multimodal tensor encoding.
    pub fn with_model_dtype(mut self, model_dtype: ModelDtype) -> Self {
-        self.model_dtype = model_dtype;
+        self.processor.model_dtype = Some(model_dtype);
        self
    }

@@ -172,57 +256,23 @@ impl ChatLlm {
    }

    /// Render, tokenize, and submit one chat request.
-    pub async fn chat(&self, mut request: ChatRequest) -> Result<ChatEventStream> {
-        request.validate()?;
-
-        // Stamp before rendering so render and tokenize count toward TTFT/e2e.
-        let arrival_time = vllm_llm::current_unix_timestamp_secs();
-
-        let output_processor = self.backend.new_chat_output_processor(
-            &mut request,
-            NewChatOutputProcessorOptions {
-                tool_call_parser: &self.tool_call_parser,
-                reasoning_parser: &self.reasoning_parser,
-            },
-        )?;
-        let rendered = self.backend.chat_renderer().render(&request)?;
-        let reasoning_parser_kwargs =
-            request
-                .sampling_params
-                .structured_outputs
-                .is_some()
-                .then(|| ReasoningParserKwargs {
-                    chat_template_kwargs: rendered.effective_template_kwargs.clone(),
-                });
-
-        let (prompt, mm_features) = multimodal::finalize_rendered_prompt(
-            &request,
-            rendered,
-            self.backend.multimodal_model_info(),
-            self.model_dtype,
-        )
-        .await?;
-
-        let text_request = TextRequest {
-            request_id: request.request_id.clone(),
-            prompt,
-            mm_features,
-            sampling_params: request.sampling_params,
-            decode_options: request.decode_options,
-            intermediate: request.intermediate,
-            priority: request.priority,
-            cache_salt: request.cache_salt,
-            add_special_tokens: request.add_special_tokens,
-            data_parallel_rank: request.data_parallel_rank,
-            reasoning_parser_kwargs,
-            lora_request: request.lora_request,
-            arrival_time: Some(arrival_time),
-        };
+    pub async fn chat(&self, request: ChatRequest) -> Result<ChatEventStream> {
+        let (text_request, output_processor) = self
+            .processor
+            .prepare(
+                request,
+                NewChatOutputProcessorOptions {
+                    tool_call_parser: &self.tool_call_parser,
+                    reasoning_parser: &self.reasoning_parser,
+                },
+            )
+            .await?;
+        let request_id = text_request.request_id.clone();
        let decoded_stream = self.text.generate(text_request).await?.map_err(Error::from).boxed();

        let structured_stream = output_processor.process(decoded_stream)?;

-        Ok(ChatEventStream::new(request.request_id, structured_stream))
+        Ok(ChatEventStream::new(request_id, structured_stream))
    }

    /// Render through the chat template and tokenize, without submitting to the engine.
@@ -233,14 +283,9 @@ impl ChatLlm {
    pub async fn tokenize_chat(&self, request: ChatRequest) -> Result<Vec<u32>> {
        request.validate()?;

-        let rendered = self.backend.chat_renderer().render(&request)?;
-        let (prompt, _mm_features) = multimodal::finalize_rendered_prompt(
-            &request,
-            rendered,
-            self.backend.multimodal_model_info(),
-            self.model_dtype,
-        )
-        .await?;
+        let rendered = self.processor.backend.chat_renderer().render(&request)?;
+        let (prompt, _mm_features) =
+            self.processor.finalize_rendered_prompt(&request, rendered).await?;

        let tokenizer = self.text.tokenizer();
        let token_ids = match prompt {
@@ -74,7 +74,7 @@ impl DefaultChatOutputProcessor {
            Box::new(CombinedParser::new(reasoning_parser, tool_parser)) as Box<dyn UnifiedParser>
        };

-        apply_structural_tag_constraint(request, parser.structural_tag_model())?;
+        apply_structural_tag_constraint(request, parser.structural_tag_builder())?;

        if parser.preserve_special_tokens() {
            request.decode_options.skip_special_tokens = false;
@@ -7,7 +7,8 @@ use thiserror_ext::AsReport;
 use vllm_engine_core_client::protocol::structured_outputs::{
    StructuredOutputBackend, StructuredOutputsParams,
 };
-use vllm_parser::tool::StructuralTagModel;
+use vllm_parser::tool::StructuralTagBuilder;
+use xgrammar_structural_tag::builders::StructuralTagOptions;
 use xgrammar_structural_tag::{
    FunctionDefinition, FunctionToolParam, ToolChoice as StructuralTagToolChoice, ToolParam,
    build_structural_tag,
@@ -20,9 +21,9 @@ use crate::{Error, Result as ChatResult};
 /// support and the request's tool choice.
 pub(super) fn apply_structural_tag_constraint(
    request: &mut ChatRequest,
-    model: Option<StructuralTagModel>,
+    builder: Option<&dyn StructuralTagBuilder>,
 ) -> ChatResult<()> {
-    let Some(model) = model else {
+    let Some(builder) = builder else {
        return Ok(());
    };
    let Some(tool_choice) = structural_tag_tool_choice(request) else {
@@ -42,11 +43,16 @@ pub(super) fn apply_structural_tag_constraint(
        })
        .collect::<Vec<_>>();

-    let structural_tag = build_structural_tag(model, &tools, tool_choice, false)
-        .and_then(|tag| tag.to_json_string())
-        .map_err(|error| Error::StructuralTag {
-            message: error.to_report_string(),
-        })?;
+    let structural_tag = build_structural_tag(
+        builder,
+        &tools,
+        tool_choice,
+        StructuralTagOptions::default().with_reasoning(false),
+    )
+    .and_then(|tag| tag.to_json_string())
+    .map_err(|error| Error::StructuralTag {
+        message: error.to_report_string(),
+    })?;

    // Overwrite any existing structured output settings with the structural tag constraint.
    request.sampling_params.structured_outputs = Some(StructuredOutputsParams {
@@ -141,7 +147,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Auto, vec![chat_tool("search", Some(true))]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request);
@@ -154,7 +160,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Auto, vec![chat_tool("search", None)]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag decision should succeed");

        assert!(request.sampling_params.structured_outputs.is_none());
@@ -169,7 +175,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag should build");

        let params = structured_outputs(&request);
@@ -184,7 +190,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Required, vec![chat_tool("search", None)]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request);
@@ -201,7 +207,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag should build");

        let params = structured_outputs(&request);
@@ -221,7 +227,7 @@ mod tests {
        );
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request).to_string();
@@ -234,7 +240,7 @@ mod tests {
        let mut request = request(ChatToolChoice::None, vec![chat_tool("search", Some(true))]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag decision should succeed");

        assert!(request.sampling_params.structured_outputs.is_none());
@@ -249,7 +255,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
            .expect("structural tag decision should succeed");

        let params = structured_outputs(&request);
@@ -26,18 +26,21 @@ const RESET: &str = "\x1b[0m";
 const VLLM_TIME_FORMAT: &[time::format_description::FormatItem<'static>] =
    format_description!("[month]-[day] [hour]:[minute]:[second]");

-const PROCESS_LABEL: &str = "RustFrontend";
-
 /// Install the process-wide vLLM-style tracing subscriber for the CLI binary.
-pub(crate) fn init_tracing() {
+pub(crate) fn init_tracing(process_label: &str) {
    let filter = build_targets_filter(
        env::var("VLLM_LOGGING_LEVEL").ok().as_deref(),
        env::var("RUST_LOG").ok().as_deref(),
    );
-    let formatter = VllmEventFormatter::new();
+    let formatter = VllmEventFormatter::new(process_label);

    let _ = tracing_subscriber::registry()
-        .with(tracing_subscriber::fmt::layer().event_format(formatter).with_filter(filter))
+        .with(
+            tracing_subscriber::fmt::layer()
+                .event_format(formatter)
+                .with_writer(std::io::stderr)
+                .with_filter(filter),
+        )
        .try_init();
 }

@@ -94,9 +97,9 @@ struct VllmEventFormatter {
 }

 impl VllmEventFormatter {
-    fn new() -> Self {
+    fn new(process_label: &str) -> Self {
        Self {
-            prefix: format!("({} pid={})", PROCESS_LABEL, process::id()),
+            prefix: format!("({process_label} pid={})", process::id()),
            timer: VllmLocalTimer::default(),
        }
    }
@@ -291,6 +294,13 @@ fn map_python_log_level(level: &str) -> LevelFilter {
 mod tests {
    use super::*;

+    #[test]
+    fn formatter_prefix_uses_process_label() {
+        let formatter = VllmEventFormatter::new("Bench");
+
+        assert_eq!(formatter.prefix, format!("(Bench pid={})", process::id()));
+    }
+
    #[test]
    fn rust_log_target_overrides_are_merged_with_vllm_default_level() {
        let filter = build_targets_filter(Some("DEBUG"), Some("hyper=warn,tower=error"));
@@ -5,6 +5,7 @@ mod cli;
 mod logging;

 use std::env;
+use std::ffi::OsStr;
 use std::process::ExitStatus;

 use anyhow::{Context, Result, anyhow, bail};
@@ -82,7 +83,14 @@ fn shutdown_signal() -> CancellationToken {
 }

 fn main() -> Result<()> {
-    logging::init_tracing();
+    let process_label =
+        match env::args_os().nth(1).as_deref().and_then(OsStr::to_str).unwrap_or_default() {
+            "bench" => "Bench",
+            "serve" | "frontend" => "RustFrontend",
+            _ => "Rust",
+        };
+    logging::init_tracing(process_label);
+
    let cli = Cli::parse();

    let mut runtime = tokio::runtime::Builder::new_multi_thread();
@@ -460,6 +460,12 @@ impl EngineCoreClient {
        self.inner.is_healthy()
    }

+    /// Subscribe to engine health changes. The current value is `true` while
+    /// the client is healthy and changes permanently to `false` on failure.
+    pub fn subscribe_health(&self) -> tokio::sync::watch::Receiver<bool> {
+        self.inner.subscribe_health()
+    }
+
    /// Return the first persistent health error observed by the client, if any.
    pub fn health_error(&self) -> Option<Arc<Error>> {
        self.inner.health_error()
@@ -9,7 +9,7 @@ use arc_swap::ArcSwapOption;
 use parking_lot::Mutex;
 use thiserror_ext::AsReport as _;
 use tokio::runtime::Handle;
-use tokio::sync::mpsc;
+use tokio::sync::{mpsc, watch};
 use tracing::{debug, info, trace, warn};
 use vllm_metrics::METRICS;
 use zeromq::RouterSendHalf;
@@ -36,6 +36,7 @@ pub(crate) struct ClientInner {
    request_reg: Mutex<RequestRegistry>,
    utility_reg: Mutex<UtilityRegistry>,
    health_error: ArcSwapOption<Error>,
+    health_tx: watch::Sender<bool>,
 }

 impl ClientInner {
@@ -57,6 +58,7 @@ impl ClientInner {
            request_reg: Mutex::new(RequestRegistry::new(engines)),
            utility_reg: Mutex::new(UtilityRegistry::default()),
            health_error: ArcSwapOption::empty(),
+            health_tx: watch::Sender::new(true),
        }
    }

@@ -169,6 +171,7 @@ impl ClientInner {
    /// persistent health error.
    pub fn close_registries(&self, error: Arc<Error>) {
        let persistent_error = self.record_health_error(error);
+        self.publish_unhealthy();
        let request_senders = self.request_reg.lock().close();
        let utility_senders = self.utility_reg.lock().close();

@@ -191,6 +194,12 @@ impl ClientInner {
        self.health_error.load().is_none()
    }

+    /// Subscribe to engine health changes. The current value is `true` while
+    /// the client is healthy and changes permanently to `false` on failure.
+    pub fn subscribe_health(&self) -> watch::Receiver<bool> {
+        self.health_tx.subscribe()
+    }
+
    /// Resolve one utility output to the waiting caller. Returns `true` if a
    /// waiting caller existed.
    pub fn resolve_utility_output(&self, output: UtilityOutput) -> bool {
@@ -280,6 +289,11 @@ impl ClientInner {
            .expect("health error must be recorded before registries close")
    }

+    /// Publish the sticky healthy-to-unhealthy transition.
+    fn publish_unhealthy(&self) {
+        self.health_tx.send_if_modified(|healthy| std::mem::replace(healthy, false));
+    }
+
    /// Assert there is a recorded health error and return a `Shared` variant
    /// wrapping it for error returns when the client is already closed.
    fn closed_error(&self) -> Error {
@@ -461,13 +475,18 @@ mod tests {
    #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
    async fn close_registries_records_first_health_error_only() {
        let inner = test_inner().await;
+        let mut health = inner.subscribe_health();
+        assert!(*health.borrow());

        inner.close_registries(Arc::new(Error::EngineCoreDead));
+        health.changed().await.expect("health sender remains open");
        assert!(!inner.is_healthy());
+        assert!(!*health.borrow());
        assert!(matches!(
            inner.health_error().as_deref(),
            Some(Error::EngineCoreDead)
        ));
+        assert!(!*inner.subscribe_health().borrow());

        inner.close_registries(Arc::new(client_closed!("shutdown")));
        assert!(matches!(
@@ -269,6 +269,19 @@ impl WireLogprobs {
            );
        }

+        // Empty position lists may be encoded as either [0, 0] or [0, k + 1].
+        if token_ids.rows == 0 {
+            return Ok(Logprobs {
+                positions: Vec::new(),
+            });
+        }
+        if token_ids.cols == 0 {
+            bail_ext_value_decode!(
+                "{field_prefix}: zero-column logprobs payload with {} rows",
+                token_ids.rows
+            );
+        }
+
        let mut positions = Vec::with_capacity(token_ids.rows);
        for ((token_ids_row, logprobs_row), sampled_rank) in token_ids
            .data
@@ -303,3 +303,49 @@ fn rejects_non_none_cu_num_generated_tokens() {
        "messagepack ext value decode failed: new_logprobs.cu_num_generated_tokens: expected None for per-request engine-core logprobs payload, got [0, 1]"
    );
 }
+
+#[test]
+fn decodes_zero_row_logprobs_as_empty() {
+    for shape in [[0usize, 0], [0, 3]] {
+        let frames = vec![Bytes::from(encode_value(&output_wire_with_custom_fields(
+            None,
+            Some(Value::Array(vec![
+                ndarray_value("<i8", &shape, Value::Ext(3, Vec::new())),
+                ndarray_value("<f4", &shape, Value::Ext(3, Vec::new())),
+                ndarray_value("<i8", &[0], Value::Ext(3, Vec::new())),
+                Value::Nil,
+            ])),
+        )))];
+        let decoded = decode_engine_core_outputs(&frames).unwrap().into_request_batch().unwrap();
+        let logprobs = decoded.outputs[0]
+            .new_prompt_logprobs_tensors
+            .clone()
+            .unwrap()
+            .into_direct()
+            .unwrap();
+        assert!(logprobs.is_empty());
+    }
+}
+
+#[test]
+fn rejects_zero_column_logprobs_with_rows() {
+    let ranks = Value::Ext(3, vec![1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]);
+    let frames = vec![Bytes::from(encode_value(&output_wire_with_custom_fields(
+        Some(Value::Array(vec![
+            ndarray_value("<i8", &[2, 0], Value::Ext(3, Vec::new())),
+            ndarray_value("<f4", &[2, 0], Value::Ext(3, Vec::new())),
+            ndarray_value("<i8", &[2], ranks),
+            Value::Nil,
+        ])),
+        None,
+    )))];
+
+    let error = decode_engine_core_outputs(&frames).unwrap_err();
+    let crate::error::Error::ExtValueDecode { message } = &error else {
+        panic!("expected ExtValueDecode");
+    };
+    assert_eq!(
+        message,
+        "new_logprobs: zero-column logprobs payload with 2 rows"
+    );
+}
@@ -4,7 +4,7 @@
 use std::sync::Arc;

 use vllm_parser::tool::{
-    Result, StructuralTagModel, Tool, ToolParser, ToolParserError, ToolParserOutput,
+    Result, StructuralTagBuilder, Tool, ToolParser, ToolParserError, ToolParserOutput,
 };
 use vllm_parser::unified::{
    UnifiedParser, UnifiedParserError, UnifiedParserEvent, UnifiedParserOutput,
@@ -85,8 +85,8 @@ impl<T: UnifiedParser> ToolParser for UnifiedToolParserAdapter<T> {
        self.inner.preserve_special_tokens()
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        self.inner.structural_tag_model()
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        self.inner.structural_tag_builder()
    }

    fn tool_call_id(&self, tool_index: usize) -> Option<&str> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekDsmlToolParser, DsmlTokens};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3.2 models.
 ///
@@ -47,8 +47,8 @@ impl ToolParser for DeepSeekV32ToolParser {
        true
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::DeepSeekV32)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::DeepSeekV32.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekDsmlToolParser, DsmlTokens};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V4 models.
 ///
@@ -50,8 +50,8 @@ impl ToolParser for DeepSeekV4ToolParser {
        true
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::DeepSeekV4)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::DeepSeekV4.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -73,7 +73,7 @@ mod tests {

    use super::DeepSeekV4ToolParser;
    use crate::tool::test_utils::{collect_stream, test_tools};
-    use crate::tool::{StructuralTagModel, ToolParser, ToolParserTestExt as _};
+    use crate::tool::{ToolParser, ToolParserTestExt as _};

    fn build_tool_call(function_name: &str, params: &[(&str, &str)]) -> String {
        let params = params
@@ -91,13 +91,10 @@ mod tests {
    }

    #[test]
-    fn deepseek_v4_exposes_structural_tag_model() {
+    fn deepseek_v4_exposes_structural_tag_builder() {
        let parser = DeepSeekV4ToolParser::new(&test_tools());

-        assert_eq!(
-            parser.structural_tag_model(),
-            Some(StructuralTagModel::DeepSeekV4)
-        );
+        assert!(parser.structural_tag_builder().is_some());
    }

    #[test]
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekJsonFormat, DeepSeekJsonToolParser};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3 JSON-fenced tool calls.
 ///
@@ -35,8 +35,8 @@ impl ToolParser for DeepSeekV3ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::DeepSeekR1)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::DeepSeekR1.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekJsonFormat, DeepSeekJsonToolParser};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3.1 raw JSON tool calls.
 ///
@@ -31,8 +31,8 @@ impl ToolParser for DeepSeekV31ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::DeepSeekV31)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::DeepSeekV31.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{GlmXmlToolParser, Separator};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for GLM-4.7 MoE XML-style tool calls.
 ///
@@ -25,8 +25,8 @@ impl ToolParser for Glm47MoeToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::Glm47)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::Glm47.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -10,7 +10,7 @@ use winnow::token::{literal, rest, take_until};
 use super::parameters::ToolSchemas;
 use super::utils::{MarkerScanState, parse_buffered_event, safe_text_len, take_until_marker};
 use super::{Result, ToolCallDelta, ToolParser, ToolParserOutput};
-use crate::tool::{StructuralTagModel, Tool};
+use crate::tool::{StructuralTagBuilder, Tool};

 const TOOL_CALLS_START: &str = "<tool_calls>";
 const TOOL_CALLS_END: &str = "</tool_calls>";
@@ -116,8 +116,8 @@ impl ToolParser for HyV3ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::HyV3)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::HyV3.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{JsonToolCallConfig, JsonToolCallParser, JsonToolCallWhitespace};
-use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};

 const HERMES_CONFIG: JsonToolCallConfig = JsonToolCallConfig {
    parser_name: "Hermes",
@@ -48,8 +48,8 @@ impl ToolParser for HermesToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
-        Some(StructuralTagModel::Hermes)
+    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+        Some(xgrammar_structural_tag::Model::Hermes.builder())
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
--- a/Show More
+++ b/Show More