lesj0610andClaude Opus 5
3b1911c0ca
Three initialization and config defects found serving Qwen4Exp, each with a regression test. **The MTP hidden buffer lands on the ambient device.** torch.empty for the spec-decode hidden buffer takes no device, so it follows whatever device is current rather than vllm_config.device_config.device. The allocation was duplicated verbatim in the NVIDIA and AMD backends; it moves to common/mtp.py so the two cannot drift, and the caller passes is_last_rank rather than reaching for the process group from shared code. **M-RoPE keys are reported as unrecognized.** Qwen4Exp drives M-RoPE through mrope_section and mrope_interleaved, which transformers' rope validator does not know for rope_type="default", so every load warns about a configuration the model genuinely uses. Declare them at rope validation on the text config and on the outer config that mirrors it; every other key still warns. **transformers' layer_types spelling is unread.** Its Qwen4Exp config canonicalizes "full_attention" entries to "qwen_sparse_attention" in memory, so checkpoints re-exported through it serialize that name. vLLM keys QSA off "full_attention" plus indexer_n_heads, so map the spelling back. **Qwen4Exp is missing from the Triton warmup set.** Its GDN prefill and QSA run on other backends, but the kernels this warmup covers -- the gated RMS norm, the causal conv, the post-conv preparation, the decode state update -- are Triton whichever prefill kernel is chosen, so they compile on the first prompt instead. qwen4_exp_mtp stays out: kernel_warmup passes the worker's own model_config, which is the target model's. tests/models/qwen4_exp/test_config.py: 15 passed. tests/model_executor/test_qwen_triton_warmup.py: 6 passed. ruff check, ruff format --check and mypy (Python 3.10) report what they already report on the base commit. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
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🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us.
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, 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:
- 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