vLLM

vLLM v0.26.0 Release Notes

Highlights

This release features 411 commits from 212 contributors (61 new)!

• New Inkling model family with a full support stack: base modeling (#48799), piecewise CUDA graph support (#48822), Hopper FA4 relative attention (#48858), MTP=1 speculative decoding (#48869), LoRA (#48884), and standard ModelOpt NVFP4 quantization (#48990).
• DeepSeek-V4 performance push across vendors: a specialized routing kernel (2.94% E2E TPOT, #48660), fused_topk_bias (1.5–2x kernel, #47463), and redundant repeat/copy removal (1.8% E2E TPOT, #48137), plus ROCm two-stage compressor for HCA prefill (#47718), sparse decode/prefill optimizations (#48519, #48788, #46275), and DSpark speculative decoding on AMD (#47419) and XPU (#47677).
• fp32 lm_head for generation models via head_dtype (#48390), extended to the LoRA path (#48525) and given a ROCm torch.mm fast path (#48688), improving accuracy for generation heads.
• Flexible attention backends: the attention backend can now be selected per KV-cache group (#48012), and sliding-window support is now an explicit backend capability (#48011) — improving support for hybrid models.
• KV offloading & tiered secondary storage matured substantially: offloading metrics (#45958, #47666, #47679), tier-owned event handling (#46544, #47923), object-store secondary tier with workload identity (#47063, #47274, #48150), DP-replica-aware tiering (#47987), and encoder-cache (EC) connectors including CPU offloading (#42433, #47423).
• Rust frontend gained multimodal video (#47959) and audio (#48554), a Seed-OSS tool parser (#47741), and a native vllm-bench port (#48107).
• Transformers 5.13.0 (#47867) with more models migrated to the Transformers modeling backend: Olmo/Olmo2 (#48100), MistralLarge3 (#48153), and HunyuanVL (#47872).

Model Support

• New models: Inkling family (#48799, #48822, #48858, #48869, #48884, #48990), BertForMaskedLM (#48463), RobertaForTokenClassification / XLMRobertaForTokenClassification (#47991), LongCat-Flash-Lite n-gram embedding (#47857), Cosmos3 Edge Reasoner (#48291) and Cosmos3-Super registration (#48211), TranslateGemma-12b-it (#41599).
• Transformers backend migrations: Olmo/Olmo2 (#48100), MistralLarge3 to AutoWeightsLoader (#48153), HunyuanVL native transformers processor for transformers 5.13 (#47872).
• GLM5.2: migrate MoE sequence-parallel support to the non-torch-compiled path (#47881).
• LoRA: FlashInfer MoE LoRA for BF16 models (#48632), LoRA for tower/connector in LlavaNextVideo (#48594), fp32 lm_head on the LoRA path (#48525), optimized TrtLlmLoRAExperts (#48759).
• Multimodal: automatic fallback to ViT data parallelism when TP is unavailable (#49046).
• Fixes: correct pooling scores for chunked prefill under torch.compile (#48901).

Engine Core

• fp32 lm_head for generation models via head_dtype (#48390); lower memory for capturing large CUDA graph sizes (#48483); opt-in persistence and reuse of the memory-profiling result across boots (#47388); improved InstantTensor loading (#46868).
• Attention: select a different attention backend per KV-cache group (#48012); sliding-window as an explicit backend capability (#48011); KV-cache layout refactor packing K/V into the content dim across backends (#44455); MRV2 virtual-batch PCP for MLA (#46570).
• Speculative decoding: runtime draft weight update (#46725), hybrid (SWA + full attention) DFlash drafters (#47914), SWA support for qwen-eagle3 (#47568), Gemma4-12B DSpark draft model (#47216), DSv4 DSpark on AMD (#47419), separate kv_cache_dtype for speculative_config (#48787).
• KV offloading: basic offloading metrics (#45958), split CPU cache usage into read/write gauges (#47666) and tiering-lookup-delay into sync/async histograms (#47679), tier-owned event handling and BlockStored events (#46544, #47923), object-store secondary tier with workload identity (#47063, #47274, #48150), DP-replica-aware tiering (#

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Easy, fast, and cheap LLM serving for everyone

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| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |

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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.

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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, 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.

• Installation
• Quickstart
• List of Supported Models

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

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• 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

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Media Kit

• If you wish to use vLLM's logo, please refer to our media kit repo