forked from Karylab-cklius/vllm
Alexander MatveevandAlexander Matveev
e3b4fdaf5d
Port SGLang's push-based 2-buffer allreduce protocol into vLLM as a new communicator backend for small-message reductions. The push protocol eliminates the two explicit cross-GPU NVLink barrier round-trips used by the existing barrier-based CustomAllreduce, replacing them with a sentinel-based data arrival detection mechanism and double-buffered epoch alternation. Key advantages over the barrier-based approach: - Zero barriers: data arrival IS the synchronization (positive-zero sentinel) - Single NVLink round-trip instead of two barrier exchanges + remote reads - All SMs active (SM_count CTAs vs 2 CTAs) for higher NVLink bandwidth - No cudaMemcpy to IPC staging buffer in eager mode - PDL (griddepcontrol) support for kernel overlap on sm_90+ The new PushAllReduce is inserted in the CudaCommunicator dispatch chain above the existing CustomAllreduce for messages below a size threshold (~720 KB at TP=8). Larger messages continue to use the barrier-based path. The existing CustomAllreduce code is not modified. Measured results on DeepSeek-V4-Pro (61 layers, TP=8, 8x NVIDIA B200, BS=1, decode with ISL=4, OSL=33024): - Throughput: +2.14% (84.06 vs 82.30 tokens/s) - TPOT: -2.09% (11.90 vs 12.15 ms/token) Correctness verified via lm_eval gsm8k 5-shot with no regression (exact_match delta within statistical noise). The feature can be disabled at runtime via VLLM_DISABLE_PUSH_ALLREDUCE=1 to fall back to the barrier-based path. Signed-off-by: Alexander Matveev <amatveev@redhat.com>
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About
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.
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 Hugging Face, 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.
Getting Started
Install vLLM with uv (recommended) or pip:
uv pip install vllm
Or build from source for development.
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations. Please check out Contributing to vLLM for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
Contact Us
- For technical questions and feature requests, please use GitHub Issues
- For discussing with fellow users, please use the vLLM Forum
- For coordinating contributions and development, please use Slack
- For security disclosures, please use GitHub's Security Advisories feature
- For collaborations and partnerships, please contact us at collaboration@vllm.ai
Media Kit
- If you wish to use vLLM's logo, please refer to our media kit repo