forked from Karylab-cklius/vllm
Yongye ZhuandClaude Opus 4.8
6542ed479c
Extend DeepGemmExperts to support MXFP8 activations (FP8 e4m3 + UE8M0 1x32 block scales) via the grouped GEMM with recipe (1, 32), reusing the oracle/fp8 weight-conversion path. Generalize deepgemm_post_process_fp8_weight_block to derive the transform recipe from the block shape ((1, 1, 32) for MXFP8) and accept uint8 E8M0 scales. Unify the fused gated-activation+quant triton kernels around y = (up + beta) * gate * sigmoid(alpha * gate): silu is alpha=1, beta=0 (bit-identical to before); swigluoai uses alpha/beta from config. Thread gemm1_alpha/gemm1_beta from the FusedMoE layer through the MXFP8 quant config into the kernels, and add swiglu_alpha/swiglu_beta to the layer and MiniMax M3 config/model (beta sourced from config, not hardcoded). Wire Fp8MoeBackend.DEEPGEMM into the MXFP8 oracle (selectable via --moe-backend deep_gemm), resolving directly to DeepGemmExperts (the Triton fallback cannot handle the 1x32 scheme). Advertise SWIGLUOAI in _supports_activation so swigluoai selects DeepGEMM rather than falling through to another backend; gate the MXFP8 scheme to Blackwell (SM100). Verified on GB200: packed-kernel parity (silu defaults unchanged, swigluoai matches torch ref), (1,32) weight-prep transform, and a TP=4 launch selecting the DEEPGEMM MXFP8 backend with full weight load (the run then stops at the still-stubbed attention forward, as expected). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Signed-off-by: Yongye Zhu <yongye@inferact.ai> Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
Easy, fast, and cheap LLM serving for everyone
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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