yangzhuxinyzxandClaude Opus 5.5
3392fec044
* [Perf][SM70] 4096-token prefill chunks: native Q4096 79T attention and reserved long-prefix workspaces The 79T Q8000/Q8192 prefill attention needs budgets of 8000+, so 4096-token chunks fell back to Split-D at ~47 TFLOPS for long prefixes (250K TTFT +22%). Add a native Q4096 specialization of the same FP32-accumulated architecture (256-token tail, own namespace, workspace slots and a half-height score buffer), dispatched for Q4000..Q4096 once KV reaches 8192 keys; Q4000..Q4095 are leading-padded. Operator medians at KV128K/248K are ~62 TFLOPS. Reserve the E4M3 prefix bridge (for max_model_len) and the bridge tail during memory profiling instead of growing them by doubling at the first long prompt, which ran a 128K request out of memory at 4096-token chunks. The Qwen3.8-27B NVFP4 + DFlash2 release profile now uses 4096-token chunks and 0.86 memory utilization and supports V100-SXM2 16GB: 4.07 GiB of KV per GPU against 3.33 GiB for 8192/0.92, TTFT within 1% to 32K and +3.9% at 250K, decode and speed-fixture rounds unchanged, 250K + four concurrent 64K requests without OOM. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * [Style] Format the 79T owner dispatch and type the Q8000 prefill reason clang-format reflows PrefillOwner::run; mypy needs the optional reason declared before its branches. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.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