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fix(deps): update dependency vllm to >=0.26.0,<0.27 - #449

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fix(deps): update dependency vllm to >=0.26.0,<0.27#449
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This PR contains the following updates:

Package Change Age Confidence
vllm >=0.22,<0.23 → >=0.26.0,<0.27 age confidence

Release Notes

vllm-project/vllm (vllm)

v0.26.0

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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 (#​47987), blocks_per_chunk config for heterogeneous KV groups (#​48878), P2P default host/port env vars (#​47636).
  • Caching: partial prefix-cache hit for hybrid models (#​46384), selective hybrid cache retention (#​47782), report prefix-cache-reused blocks in full report mode (#​45261).
  • Reasoning: optimize TPOT for thinking budget when used with speculative decoding (#​46662).
  • RLHF: stateful trainer-send abstractions (#​48042).
  • Fixes: host memory leak from undrained new_block_ids (#​44490), DSv3.2 + MTP + sequence-parallel accuracy (#​48036).
Hardware & Performance
  • DeepSeek-V4: specialized routing kernel (2.94% E2E TPOT, #​48660), fused_topk_bias 1.5–2x (#​47463), redundant repeat/copy removal (1.8% TPOT, #​48137).
  • MoE router GEMMs: BF16x3 router GEMM (#​47973), FP32 router GEMV (#​48335), generic CuteDSL LL BF16 router GEMM (#​42562); TRTLLM BF16 MoE modular kernel (#​45182); write FlashInfer combine into final output (#​47156).
  • Qwen: fuse more RMSNorm + all-reduce in Qwen3.5 (#​46998), replace MoE all-reduce with reduce-scatter (#​47006), Qwen3.5 H20 optimization (#​48350), expand Triton warmup coverage (#​47546).
  • MLA: dense MHA path for short sparse-MLA sequences (#​47327); MiniMax-M3 long-context decode indexer on sm100 (#​48582).
  • Kernels: CUDA kernel for ReLUSquaredActivation / relu^2 (#​39058), Helion kernel lazy registration (#​48264), vectorize _copy_mamba_state_block to uint64 (#​48110), stop upcasting logits to fp32 in the sampler (#​48641).
  • ROCm: fp32 head_dtype torch.mm fast path (#​48688), DSv4 two-stage compressor kernel (#​47718), sparse decode/prefill optimizations (#​48519, #​48788, #​46275), DSv3.2 sparse MLA KV-split heuristic (#​46832) and MTP CUDA-graph mode (#​45149), MXFP8 GEMM for MiniMax-M3 (#​46117), AITER sparse paged attention + spec decode for MiniMax-M3 (#​47287, #​47984), MiniMax-M2 fused QK-norm + all-reduce via AITER (#​44849), HybridW4A16 linear kernel (#​40977), Qwen3-30B-A3B QK-Norm+RoPE+KV runtime fusion (#​42749).
  • XPU: batch-invariant kernels (#​41934), HND KV layout support (#​47975), DSpark spec decode for DSv4 (#​47677), nightly/release image publishing (#​47880, #​48126).
  • CPU: DFlash speculative decoding for GDN models on CPU (#​46090), s390x NUMA topology (#​40714), native macOS arm64 CPU wheel builds (#​48289); POWER VSX math function optimization (#​47321) and IBM Power docker builds using prebuilt wheels (#​46017).
  • Distributed fusion: FlashInfer MNNVL all-reduce RMS quant fusion (#​48064).
  • Build/autotune: arm64 Blackwell SM10x/SM110 image builds (#​48041); skip CuTeDSL fp4_gemm autotuning by default (#​48268).
Large Scale Serving & Distributed
  • Decode Context Parallel (DCP): hybrid attention support (#​40996), DCP + Eagle for Tokenspeed MLA backends (#​48180).
  • PD disaggregation: NIXL pipeline-parallel prefill in push mode (#​45880).
  • Encoder-cache connectors: EC transfer params (#​42433) and CPU-offloading EC connector (#​47423).
Quantization
  • Humming w[2-7]a[4,8] weight-only inference with compressed-tensors (#​46390); int4 quantization for the emulation MoE backend (#​48451); INT2 XPU weight-only quant linear (#​47521).
  • NVFP4/MXFP4: nvfp4_per_token online MoE quantization (#​48538), CuTe-DSL FlashInfer MXFP4 quantization (#​48417); bounded peak memory when repacking FP4 MoE weights for Marlin (#​47851) and for NVFP4 MoE weight loading (#​46276).
  • MLA: kv_cache_dtype_skip_layers support (#​47309).
  • ROCm: HybridW4A16 linear kernel (#​40977).
API & Frontend
  • Rust frontend: multimodal video (#​47959) and audio (#​48554), Seed-OSS tool parser (#​47741), native vllm-bench port (#​48107), continue_final_message handling with renderer sentinel (#​47844).
  • OpenAI compatibility: bad_words in /v1/completions (#​46793), expose logprob_token_ids on Python OpenAI endpoints (#​43463), include_reasoning param for non-Harmony models (#​44301), populate num_cache_creation_tokens on Messages responses (#​48535).
  • Endpoint plugins framework (#​47454); /abort_requests on the RLHF dev API router (#​47173); Deepstream video decoding backend (#​42424); overlap preprocessing and computation for pooling models in offline inference (#​47699).
  • UX: human-readable integers for more CLI args (#​47608), CuTeDSL compilation progress bar (#​48881), expanded GPU profiler config scope/annotations (#​37524), log worker exit code when a process dies unexpectedly (#​38641).
  • Stability/correctness: handle grammar compilation failures without crashing the engine (#​47312), fix logprobs token-string collision from SentencePiece spaces (#​48674).
Security
  • Replace diskcache to eliminate pickle deserialization (#​44549).
  • Fix a concurrent sparse-invariant race that bypassed CVE remediation (#​48583).
  • Add resource-bounds validation to derender endpoints (#​47260); sanitize server file paths from validation error responses (#​46415); bound the completion prompt list to prevent unbounded engine fan-out (#​47845); guard lm-format-enforcer regex compilation with a timeout (#​47595).
Dependencies
Deprecations & Removals

New Contributors

Contributors

@​mgoin, @​yewentao256, @​NickLucche, @​njhill, @​LucasWilkinson, @​micah-wil, @​khluu, @​AndreasKaratzas, @​WoosukKwon, @​aoshen02, @​BugenZhao, @​vanshbhatia-amd, @​jperezdealgaba, @​jeejeelee, @​hmellor, @​tlrmchlsmth, @​MatthewBonanni, @​Change72, @​chaojun-zhang, @​gau-nernst, @​gnovack, @​ZJY0516, @​reidliu41, @​Yejing-Lai, @​taneem-ibrahim, @​Srinivasoo7, @​Sunt-ing, @​AmeenP, @​zhenwei-intel, @​LopezCastroRoberto, @​matteso1, @​yzong-rh, @​stefankoncarevic, @​Isotr0py, @​djramic, @​charlifu, @​peizhang56, @​giuseppegrossi, @​drakosha, @​NickCao, @​benchislett, @​Rohan138, @​hickeyma, @​muhammadfawaz1, @​rasmith, @​zixi-qi, @​music-dino, @​BWAAEEEK, @​xianbaoqian, @​wendyliu235, @​atalman, @​joerowell, @​ErenAta16, @​AlejandroParedesLT, @​gcanlin, @​liranschour, @​omerpaz95, @​Alex-ai-future, @​tanpinsiang, @​Fangzhou-Ai, @​KKothuri, @​zxd1997066, @​akii96, @​nemanjaudovic, @​Etelis, @​afierka-intel, @​DaoyuanLi2816, @​HDCharles, @​tahsintunan, @​xiaohongchen1991, @​sagearc, @​mikekg, @​kliuae, @​qli88, @​arpera, @​yushangdi, @​edwinlim0919, @​tjtanaa, @​ariG23498, @​lucifer1004, @​netanel-haber, @​kl527, @​Rukhaiya2004, @​ronensc, @​guan404ming, @​shaunkotek, @​liulanze, @​pierDipi, @​eldarkurtic, @​simon-mo, @​robinguo23, @​CienetStingLin, @​RishabhSaini, @​Sahil170595, @​jasonlizhengjian, @​jacklin78911-collab, @​walterbm, @​amd-ethany, @​zqzten, @​nicklasfrahm, @​hongxiayang, @​alexeldeib, @​ManaEstras, @​sungbin1015, @​aoright, @​Saddss, @​vivek8123, @​voipmonitor, @​shawntsai, @​almayne, @​ilmarkov, @​cleonard530, @​kjiang249, @​chaunceyjiang, @​bigPYJ1151, @​tsvikas, @​deng451e, @​tvirolai-amd, @​zhewenl, @​zihaomu, @​ap9272, @​staugust, @​Yancey0623, @​GongLei-HW, @​albertoperdomo2, @​guoriyue, @​ViranjanPagar, @​Functionhx, @​XuZhou26, @​MynameFelix, @​larryli2-amd, @​ashwing, @​thisisjimmyfb, @​robertgshaw2-redhat, @​mayuyuace, @​ibondarenko1, @​zhejiangxiaomai, @​vx120, @​hugo-cen, @​tanish-malekar, @​zzt93, @​guybd, @​R3hankhan123, @​mmangkad, @​omera-nv, @​yma11, @​Gavin-Morris-04, @​pavanimajety, @​shanjiaz, @​wenpengw-nv, @​atalhens, @​langzhao-netizen, @​emricksini-h, @​Zhenzhong1, @​DanBlanaru, @​mgehre-amd, @​mwoodson, @​wangxiyuan, @​adsridhar, @​hnt2601, @​gangula-karthik, @​tomerg-nvidia, @​adhi29, @​rishitdholakia13, @​divakar-amd, @​chaeminlim-mb, @​joanvelja, @​russellb, @​janeyx99, @​aarushjain29, @​wjabbour, @​mahadrehmann, @​krishy91, @​tzielinski-habana, @​avalliappan-nvidia, @​ruikangliu, @​majian4work, @​maxyanghu, @​brijrajk, @​lengrongfu, @​Josephasafg, @​elvircrn, @​xiaguan, @​ricky-chaoju, @​iyastreb, @​ovidiusm, @​tuukkjs, @​noooop, @​samnordmann, @​AndyDai-nv, @​xiao-llm, @​DiegoCao, @​yuvalluria, @​jhu960213, @​woosebastian, @​Debasish-87, @​esmeetu, @​hao-aaron, @​zhangj1an, @​wendadawen, @​juliendenize, @​passtoor-agi, @​mosya415, @​labAxiaoming, @​devalshahamd, @​wangxingda, @​xuebwang-amd, @​fuscof-ibm, @​alexxu-roblox, @​frida-andersson, @​lishunyang12, @​izhuhaoran

v0.25.1

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vLLM v0.25.1

Highlights

This release features 2 commits from 2 contributors (1 new)!

v0.25.1 is a patch release containing two targeted bug fixes on top of v0.25.0.

Bug Fixes
  • Avoid blocking model launching when no system FFmpeg is available for TorchCodec (#​47888). Previously import torchcodec raised a RuntimeError at import time when system FFmpeg was missing, which blocked startup (e.g. vllm serve Qwen/Qwen3-VL-2B-Instruct) even when TorchCodec was not in use. The error is now deferred to runtime so it only surfaces if TorchCodec is actually needed.
  • Guard mixed-dtype allreduce RMSNorm quant fusions (#​48330). The fused FlashInfer allreduce + RMSNorm + static-quantization patterns could match graphs where the activation and RMSNorm weight dtypes differ (e.g. a BF16 residual stream with an FP32 Gemma/Qwen-style RMSNorm weight in NVFP4 models), corrupting the hidden state and producing garbage output such as repeated !!!!! tokens. A dtype-match guard now routes incompatible mixed-dtype graphs to the safe path, while same-dtype models retain the full allreduce + RMSNorm + quant fusion.

Contributors

@​Isotr0py, @​hugo-cen

New Contributors

v0.25.0

Compare Source

vLLM v0.25.0 Release Notes

Highlights

This release features 558 commits from 232 contributors (64 new)!

  • Model Runner V2 is now the default for all dense models (#​44443). Building on quantized-model support from the previous release, MRv2 is now the standard execution path, with new support for EVS (#​46535), realtime embeddings (#​46762), prefix caching for Mamba hybrid models (#​42406), multimodal-prefix bidirectional attention (#​46942), and dynamic speculative decoding compatible with full CUDA graphs (#​45953).
  • PagedAttention has been removed (#​47361). The legacy attention implementation is deleted now that V1/MRv2 backends are the standard path.
  • The Transformers modeling backend is now as fast as native vLLM (#​47187), and gained FP8 MoE support (#​46820), CUDA graph + embed scaling fixes (#​48010), and migration of GPTBigCode/Starcoder2 (#​30966) and RoBERTa (#​47452).
  • New models: LLaVA-OneVision-2 (#​44785), Unlimited OCR (#​46564, #​47102), MOSS-Transcribe-Diarize (#​47729), openai/privacy-filter (#​41026), and Hy3 (#​47192). GLM-5 / DeepSeek-V3.2 landed in the model zoo (#​46808) with GLM-5.2 tuning, and MiniMax-M3 gained pipeline parallelism (#​45810) and NVFP4 support (#​46756).
  • New Streaming Parser Engine (#​46610) — a unified tool-call/reasoning parsing framework, with a new Kimi k2.5/k2.6/k2.7 parser and ports of seed_oss (#​46314) and DeepSeek V4 (#​45877). The Rust frontend continues to mature with HTTPS/mTLS (#​45890), a DP supervisor (#​47076), and profiler control routes (#​46306).
  • Universal speculative decoding for heterogeneous vocabularies (TLI) (#​38174), plus new DSpark (#​46995) and DFlash (#​46770, #​46853) drafters.
Model Support
Engine Core

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⚠️ Artifact update problem

Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.

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The artifact failure details are included below:

File name: uv.lock
Command failed: uv lock --upgrade-package vllm
Using CPython 3.12.13 interpreter at: /opt/containerbase/tools/python/3.12.13/bin/python3
  × No solution found when resolving dependencies:
  ╰─▶ Because vllm>=0.26.0 depends on transformers>=5.5.3 and
      eval-framework[openai]==0.8.4 depends on transformers>=4.45.2,<5, we
      can conclude that eval-framework[openai]==0.8.4 and vllm>=0.26.0 are
      incompatible.
      And because only eval-framework[openai]==0.8.4 is available, we can
      conclude that vllm>=0.26.0 and all versions of eval-framework[openai]
      are incompatible.
      And because your project depends on eval-framework[openai] and
      vllm>=0.26.0, we can conclude that your project's requirements are
      unsatisfiable.

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@aar-public-version-bump-bot aar-public-version-bump-bot Bot changed the title fix(deps): update dependency vllm to >=0.24.0,<0.25 fix(deps): update dependency vllm to >=0.25.0,<0.26 Jul 15, 2026
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@aar-public-version-bump-bot aar-public-version-bump-bot Bot changed the title fix(deps): update dependency vllm to >=0.25.0,<0.26 fix(deps): update dependency vllm to >=0.25.0,<0.26 - autoclosed Jul 18, 2026
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@aar-public-version-bump-bot aar-public-version-bump-bot Bot changed the title fix(deps): update dependency vllm to >=0.25.0,<0.26 - autoclosed fix(deps): update dependency vllm to >=0.25.1,<0.26 Jul 23, 2026
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@aar-public-version-bump-bot aar-public-version-bump-bot Bot changed the title fix(deps): update dependency vllm to >=0.25.1,<0.26 fix(deps): update dependency vllm to >=0.26.0,<0.27 Jul 29, 2026
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