forked from Karylab-cklius/vllm
9167ef8dd7
The one-sided MoeAlltoAll dispatch workspace was hardcoded for nvfp4
hidden states + fp8 scales, so any other activation dtype overran the
buffer. Parameterize the workspace sizing by bytes-per-elem and whether
an fp8 scale payload is present, then route non-nvfp4 quant configs to
a bf16 dispatch (2 B/elem, no scale) via a new defer_input_quant hint.
trtllm_mxfp4 experts already advertise expects_unquantized_inputs=True
(they call mxfp8_quantize internally). Wire make_mxfp4_moe_kernel to
pass that signal into maybe_make_prepare_finalize, and have the one-
sided prepare() honor the per-call defer_input_quant flag by shipping
a1 as bf16 with no scale payload. Two-sided already handled this.
NOTE: the flashinfer moe_a2a_dispatch C++ kernel only templates top_k
in {1, 2, 4, 8}; models with other top_k (e.g. DeepSeek-V4 top_k=6)
must use flashinfer_nvlink_two_sided instead.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
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Welcome to vLLM
Easy, fast, and cheap LLM serving for everyone
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vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors.
Where to get started with vLLM depends on the type of user. If you are looking to:
- Run open-source models on vLLM, we recommend starting with the Quickstart Guide
- Build applications with vLLM, we recommend starting with the User Guide
- Build vLLM, we recommend starting with Developer Guide
For information about the development of vLLM, see:
vLLM is fast with:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with PagedAttention
- Continuous batching of incoming requests, chunked prefill, prefix caching
- Fast and flexible model execution with piecewise and full CUDA/HIP graphs
- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and more
- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton
- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL
- Speculative decoding including n-gram, suffix, EAGLE, DFlash
- Automatic kernel generation and graph-level transformations using torch.compile
- Disaggregated prefill, decode, and encode
vLLM is flexible and easy to use with:
- Seamless integration with popular Hugging Face models
- High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
- Tensor, pipeline, data, expert, and context parallelism for distributed inference
- Streaming outputs
- Generation of structured outputs using xgrammar or guidance
- 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.
vLLM seamlessly supports 200+ model architectures on HuggingFace, including:
- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)
- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5)
- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral)
- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT)
- Reward and classification models (e.g., Qwen-Math)
Find the full list of supported models here.
For more information, check out the following:
- vLLM announcing blog post (intro to PagedAttention)
- vLLM paper (SOSP 2023)
- How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al.
- vLLM Meetups