vLLM v0.23.0 Release Notes

Please note that Minimax M3 is not yet supported in this version. Please follow vLLM recipe for usage guides for M3.

Highlights

This release features 408 commits from 200 contributors (63 new)!

• DeepSeek-V4 matures across backends: Following its introduction in v0.22.0, DeepSeek-V4 received another large hardening and optimization pass. Its sparse MLA metadata is now decoupled from DeepSeek-V3.2 (#44699), it gained a TRTLLM-gen attention kernel (#43827), EPLB support for the Mega-MoE (#43339), selective prefix-cache retention for sliding-window KV cache (#43447), and an index-share feature for DSA MTP (#44420). The model was also detached from torch.compile (#43746, #43891), its attention and RoPE paths were refactored (#44569, #44262, #43926), and an XPU attention decode path was added (#42953).
• Model Runner V2 expands to more dense models: MRv2 is now selected by default for Llama and Mistral dense models (#43458) in addition to Qwen3. It gained a FlashInfer sampler (#42472), breakable CUDA graphs (#44050), pipeline-parallel bubble elimination (#42187), kernel block-size support for hybrid models (#38831), and Gemma 4 MTP (#43241).
• Rust frontend grows up: The experimental Rust frontend added a streaming generate endpoint (#43779), dynamic LoRA endpoints (#43778), /version (#43854) and /server_info (#43942) endpoints, a server-router extension hook (#43774), request-ID headers (#43883), and many new tool parsers (InternLM2 #43481, hy_v3 #43872, Phi-4-mini #44213, Gemma4 #43850).
• Gemma 4: Added encoder-free Gemma 4 Unified support (#44429) and Gemma 4 MTP (#43241), plus numerous accuracy and startup fixes.
• Transformers v5 compatibility: vLLM now targets Transformers v5, deprecating v4 support (#40389), with vendored MiniCPM-V/O processors (#44282) and compatibility fixes for Sarvam (#38804) and Voxtral (#44559).
• Multi-tier KV cache offloading: The offloading framework gained an object-store secondary tier (#41968), HMA enabled by default for capable connectors (#41847), tiering support for HMA models (#44287), and a per-request offloading policy via the on_new_request lifecycle hook (#43205).
• Unified parser: Reasoning and tool-call parsing are now unified behind a single Parser.parse() interface (#44267), with the Responses parser migrated to it (#42977).

Model Support

• New models: MiMo-V2.5 (#40967), Step-3.7-Flash (#43859), Cosmos3 Reasoner (#43356), Gemma 4 Unified encoder-free (#44429), JetBrains Mellum v2 (#43992), Granite Speech Plus (#43519), Cohere Mini Code (#44707).
• Gemma 4: Encoder-free Unified support (#44429), MTP (#43241), native ViT linear layers (#43798), vision-embedder excluded from quantization (#44571), and fixes for MTP under TP>1 (#43909), block-table mismatch under concurrency (#43982), transformers-processor startup crash (#44232), and CPU init (#44615).
• Transformers v5: Deprecate v4 support (#40389), vendor MiniCPM-V/O processors (#44282), Sarvam compat (#38804), Voxtral fetch_audio for transformers≥5.10 (#44559).
• Model fixes & enhancements: Qwen3-VL/Qwen3-omni-thinker deepstack accuracy under torch.compile (#43617), EVS for Qwen3-VL (#44205), GLM-5.1 PP loading (#42944), GLM-4.1V processor logits (#43575), GLM-4.6V video loader (#44417), OlmoHybrid init (#43846), HyperCLOVAX remote-code removal (#43860), Bailing-MoE rotary factor (#43770), Step3 PP residual KeyError (#37622), MiniCPM-V-4.6 video (#44509), MiniCPM-O audio unpadding (#38053), MiniCPM-V batched preprocessing (#44609), FunASR-Nano init (#44215), Cohere routing method (#44021), Kimi-K2.5 FlashInfer ViT metadata (#44493).
• Multimodal: Auto-select registered video loader for VLMs (#44126), O(log n) multimodal item handling per step (#44212), local image encoding in benchmarks (#43843), interleaved custom image benchmark datasets (#43636).
• Pooling/Classification: Proper exceptions for pooling UX (#44593), extra_repr() for pooler classes (#44805), LoRA-adapter-name pooling fix (#44410), resettled generative scoring entrypoint (#44153), expanded pooler unit tests (#43818, #44471).
• Refactor: AutoWeightsLoader for InternLM2 (#38278).

Engine Core

• Model Runner V2: Default for Llama and Mistral dense models (#43458), FlashInfer sampler (#42472), breakable CUDA graphs (#44050), removed Eagle's dedicated CUDA graph pool (#44078), pipeline-parallel bubble elimination (#42187), kernel block size for hybrid models (#38831), zeroing of freshly allocated KV blocks for hybrid + FP8 KV cache (#43990), actual batch max_seq_len for attention metadata (#43991), rejection-sampling acceptance-rate fix (#40651), KVConnector + PP cleanup (#43732), speculator-prefill warmup/capture (#44253).
• Speculative decoding (DFlash): Causal DFlash (#43445), proper lookahead-slot allocation (#43733), prefix-cache corruption fix (#42971); independent drafter attention-backend selection (#39930), attention-group split by num_heads_q for drafts (#43543), EAGLE/MTP lookahead caching in the SWA prefix-cache mask (#44082).
• Attention & hybrid/Mamba: FlexAttention/FlashAttention num-blocks-first layouts (#42095), OOT MLA prefill backend registration (#43325), FlashAttention upstream sync (#44065), Mamba LINEAR attention-module refactor (#43556), corrupted MLA + linear attention fix (#43961), KDA conv-state unification (#44539) and gate/cumsum fusion (#43667), Mamba SSD do_not_specialize (#43803), Qwen3.5 mixed prefill+decode split routing (#44700), MiniMax-M2 gate kernel (#38445).
• KV cache & scheduler: Pluggable KVCacheSpec (#37505), scheduler_block_size threaded into KVCacheManager/Coordinator (#44165), max_concurrent_batches moved to VllmConfig (#44274), config validation rejecting 0/negative knobs (#43794, #44057, #44207), KV-cache scale boilerplate removed from weight loading (#43167).
• Core: Freeze the garbage collector in workers after model init (#44363), sparse NCCL weight transfer for in-place updates (#40096), graceful spinloop ext-load failure handling (#43659), scheduled-function deprecations (#43358).

Large Scale Serving & Distributed

• KV cache offloading: Object-store secondary tier (#41968), HMA on by default for capable connectors (#41847) and tiering (#44287), per-request offloading policy (on_new_request) (#43205) and on_schedule_end() hook (#44206), token-offset selective offload (#39983), skip decode-phase blocks in CPU offload (#43797), page-size block alignment (#43689), Triton fast-path for small CPU→GPU swap_blocks_batch (#42212), stale sliding-window block fix (#42959).
• KV connectors / disaggregated serving: PP-aware handshake aggregation and intermediate-PP output plumbing (#43720), multiple-async-KV-load deadlock fix (#44560), Nixl Mamba prefix-caching mode (#42554), NixlConnector kv_both role deprecation cycle (#43874), Mooncake fixes (#43742, #44103, #42694), LMCache LMCacheMPConnector (#42865), EC connector shutdown API (#42423) and non-blocking lookup (#41627), KV-transfer tokens excluded from iteration_tokens_total (#43346).
• EPLB: Async EPLB by default (#43219), EPLB for DeepSeek-V4 Mega-MoE (#43339), Nixl zero-copy EPLB transfers (#41633).
• Data parallel: DP Ray placement groups on specific nodes (#44669) and grouped-node allocation fix (#43998), SSL for the DP supervisor (#43688), DP-coordinator startup timeout raised to 120s (#42343), per-GPU-worker RDMA NIC selection (#42083).

Hardware & Performance

• NVIDIA / kernels: FP8 FlashInfer attention for ViT (#38065), Triton MoE backend on Hopper by default (#44220), CUTLASS FP8 scaled-mm padding bypass (+20%) (#43706), MoE-permute buffer pre-allocation (+9–14%) (#43014), Fp8BlockScaledMM new_empty() optimization (#43677), TurboQuant shared dequant buffers (#40941), tuned selective_state_update for H200/RTX PRO (#44251), Inductor fast-path fallback for vLLM/AITER custom ops (#42129), Gemma RMS all-reduce fusion (#42646), NUMA auto-binding on DGX B300 (#43270).
• AMD ROCm: ROCm 7.2.3 (#43136), AITER v0.1.13.post1 (#44265), native W4A16 (#41394) and fused-MoE W4A16 HIP (#44075) kernels for RDNA3 (gfx1100), AITER top-k/top-p sampler by default (#43331), attention-sink support in AITER FA (#43817), AITER hipBLASLt GEMM online tuning (#40426), permute_cols for ROCm (#44674), blocks-first KV layout for AMD (#43660), N=5 wvSplitK for spec decode (#40687), MoRI connector improvements (#43303, #41751, #40344).
• Intel XPU: vllm-xpu-kernel v0.1.7 (#41019), block_fp8_moe (#42139), block-scaled W8A8 FP8 path (#39968), WNA16 oracle for GPTQ sym-int4 (#41426), rms_norm/act quant fusions (#43963), GDN-attention MTP (#43565), Triton selective-scan op (#43421), transparent sleep mode (#37149), CPU/tiering offloading on XPU (#36423), DeepSeek-V4 attention decode path (#42953).
• CPU & other architectures: zentorch-accelerated W8A8/W4A16 on AMD Zen CPUs (#41813), CPU top-k/top-p Triton sampling (#43633), non-divisible GQA decode in mixed batches (#43032), cpu_awq folded into awq_marlin (#43841), RISC-V RVV WNA16 helpers (#42730), fused GDN gated-delta-rule kernels (#43534), PowerPC SHM communicator (#43754), arm64 CI image (#41303).
• TPU: tpu-inference upgraded to v0.20.0 (#43394) then v0.21.0 (#44621).
• torch stable ABI: Continued migration of kernels to the libtorch stable ABI — merge_attn_states/mamba/sampler [8/n] (#43361), attention/cache kernels [9/n] (#43717), header files (#44013), cuda_view/silu_and_mul [10/n] (#44334), custom all-reduce/DeepSeek-V4 fused MLA/MXFP8 MoE [10b/n] (#44365); ROCm fallback to regular ABI (#44648), _has_module trial-import verification (#44035).

Quantization

• ModelOpt: LM-head quantization (#42124), MXFP8 non-gated MoE (#42958).
• compressed-tensors: WNA8O8Int linears and WNInt embeddings (#44340), asymmetric MoE WNA16 Marlin (#44025), single-class NVFP4 linear refactor (#42443).
• Kernels & backends: Triton W4A16 as CUDA fallback for non-Marlin-aligned shapes (#43731), Marlin MoE on SM 12.x (#40923), Machete W4A16 tests (#35450), fail-fast for unsupported NVFP4 KV-cache-dtype arch (#43669), CuteDSL compressor 128-split kernel optimization (#44230).
• MoE refactor (oracle): Migrated ModelOpt MXFP8 (#42768), W4A8-int8 (#42789), and WNA16 backend selection (#42553) into the modular-kernel oracle; removed supports_expert_map (#43108) and the inplace fused-experts mechanism (#43727).

API & Frontend

• Anthropic Messages API: Structured output and effort support (#42396), system-role messages inside the messages array (#44283).
• OpenAI / Responses API: system_fingerprint field (#40537), streaming tool/function calling with required (#40700), chat_template_kwargs in Responses (#43761), developer-to-system conversion in the HF renderer (#43590), unstreamed tool-call-args streaming fix (#44348).
• Parsers: Unified reasoning + tool-call parsing behind Parser.parse() (#44267), Responses parser migrated to the unified interface (#42977), unstreamed tool-arg flush moved into the parser (#44017); new/fixed tool parsers — MiniCPM5 XML (#43175), Qwen3 XML JSON-args-first (#43243), DeepSeek DSML incremental streaming (#42879), first-args-chunk serializer fix (#42683), tool_choice="none" honored in streaming (#42752), null-tool-args crash fix (#43862).
• Frontend: thinking_token_budget validation (#43402), GPT-OSS instruction rendering (#44330), Harmony stop_token_ids cleanup (#44009), consistent VLLMValidationError in chat/completion validators (#36254), consolidation of dev entrypoints (#44170) and online-serving utils (#44479).
• Rust frontend: Streaming generate endpoint (#43779), dynamic LoRA endpoints (#43778), /version (#43854) and /server_info (#43942), server-router extension hook (#43774), --enable-request-id-headers (#43883), recursive tool-parameter conversion (#44299), include_reasoning=false (#44391), --language-model-only skips the multimodal processor (#44500), per-engine batch auto-abort (#44591), UTF-8 char-boundary detokenizer fix (#44620), HF chat-template fixes (#44311), cross-DP aggregation of is_sleeping/reset_prefix_cache (#43429); new tool parsers — InternLM2 (#43481), hy_v3 (#43872), Phi-4-mini JSON (#44213), Gemma4 (#43850).
• Benchmarks: Timed trace replay for Moonshot/Alibaba workloads in vllm bench serve (#39795), reasoning-model (thinking) benchmarking via --chat-template-kwargs (#44244).

Security

• Transport encryption: SSL/TLS support for the data-parallel supervisor (#43688).
• Untrusted-input hardening: Reject out-of-vocabulary token IDs before they reach the GPU logprob path (#44042) and fix a UTF-8 char-boundary panic in the Rust incremental detokenizer on malformed input (#44620), both of which prevent request-triggered crashes.
• Parameter validation: Reject invalid thinking_token_budget values (#43402), non-positive ParallelConfig integer knobs (#44057), zero-valued config fields (#43794), and out-of-range max_num_scheduled_tokens (#44207).

Dependencies

• FlashInfer v0.6.12 (#44036), ROCm 7.2.3 (#43136), AITER v0.1.13.post1 (#44265), tpu-inference v0.21.0 (#44621), mistral-common bump (#44649), fastsafetensors v0.3.2 (#43625).
• Removed the stale cuDNN frontend upper bound (#42599); Docker fixes for flashinfer-jit-cache (#44366), FlashInfer CuTe DSL JIT libcublas-dev (#39855), and CUTLASS DSL cu13 install order (#45204).

Deprecations

• Deprecate Transformers v4 support (#40389).
• Deprecate JAISLMHeadModel (#43784).
• Begin the deprecation cycle for the NixlConnector kv_both role (#43874).
• Remove functions previously scheduled for deprecation in v0.21.0 (#43358).

New Contributors

• @aadwived made their first contribution in https://github.com/vllm-project/vllm/pull/41813
• @adhithyamulticoreware made their first contribution in https://github.com/vllm-project/vllm/pull/44615
• @adityasingh2400 made their first contribution in https://github.com/vllm-project/vllm/pull/43550
• @adotdad made their first contribution in https://github.com/vllm-project/vllm/pull/43100
• @amd-fuweiy made their first contribution in https://github.com/vllm-project/vllm/pull/43684
• @andakai made their first contribution in https://github.com/vllm-project/vllm/pull/43617
• @animeshtrivedi made their first contribution in https://github.com/vllm-project/vllm/pull/39795
• @BramVanroy made their first contribution in https://github.com/vllm-project/vllm/pull/43087
• @CienetStingLin made their first contribution in https://github.com/vllm-project/vllm/pull/43394
• @devin-lai made their first contribution in https://github.com/vllm-project/vllm/pull/44213
• @Dymasik made their first contribution in https://github.com/vllm-project/vllm/pull/43982
• @ECMGit made their first contribution in https://github.com/vllm-project/vllm/pull/43332
• @fallintoplace made their first contribution in https://github.com/vllm-project/vllm/pull/43540
• @gagandhakrey made their first contribution in https://github.com/vllm-project/vllm/pull/43792
• @galletas1712 made their first contribution in https://github.com/vllm-project/vllm/pull/43926
• @garrygale made their first contribution in https://github.com/vllm-project/vllm/pull/44205
• @Gruner-atero made their first contribution in https://github.com/vllm-project/vllm/pull/42967
• @hanlin12-AMD made their first contribution in https://github.com/vllm-project/vllm/pull/40426
• @harshaljanjani made their first contribution in https://github.com/vllm-project/vllm/pull/41459
• @Holworth made their first contribution in https://github.com/vllm-project/vllm/pull/39562
• @hoobnn made their first contribution in https://github.com/vllm-project/vllm/pull/42752
• @HueCodes made their first contribution in https://github.com/vllm-project/vllm/pull/44591
• @IdoAtadTD made their first contribution in https://github.com/vllm-project/vllm/pull/43978
• @jasonboukheir made their first contribution in https://github.com/vllm-project/vllm/pull/41426
• @Jie-Fang made their first contribution in https://github.com/vllm-project/vllm/pull/43584
• @JINO-ROHIT made their first contribution in https://github.com/vllm-project/vllm/pull/43830
• @JMonde made their first contribution in https://github.com/vllm-project/vllm/pull/37622
• @JohnQinAMD made their first contribution in https://github.com/vllm-project/vllm/pull/43331
• @jwzheng96 made their first contribution in https://github.com/vllm-project/vllm/pull/44057
• @Kartavyasonar made their first contribution in https://github.com/vllm-project/vllm/pull/43669
• @Krishnachaitanyakc made their first contribution in https://github.com/vllm-project/vllm/pull/38053
• @linzm1007 made their first contribution in https://github.com/vllm-project/vllm/pull/43402
• @MaciejBalaNV made their first contribution in https://github.com/vllm-project/vllm/pull/43356
• @Majid-Taheri made their first contribution in https://github.com/vllm-project/vllm/pull/43803
• @mfylcek made their first contribution in https://github.com/vllm-project/vllm/pull/43421
• @MHYangAMD made their first contribution in https://github.com/vllm-project/vllm/pull/42595
• @mikekg made their first contribution in https://github.com/vllm-project/vllm/pull/43330
• @nightcityblade made their first contribution in https://github.com/vllm-project/vllm/pull/44118
• @NolanHo made their first contribution in https://github.com/vllm-project/vllm/pull/43774
• @oguzhankir made their first contribution in https://github.com/vllm-project/vllm/pull/41759
• @okorzh-amd made their first contribution in https://github.com/vllm-project/vllm/pull/42129
• @Oxygen56 made their first contribution in https://github.com/vllm-project/vllm/pull/44236
• @QiliangCui2023 made their first contribution in https://github.com/vllm-project/vllm/pull/44476
• @rajkiranjoshi made their first contribution in https://github.com/vllm-project/vllm/pull/42083
• @Rukhaiya2004 made their first contribution in https://github.com/vllm-project/vllm/pull/43754
• @ruocco made their first contribution in https://github.com/vllm-project/vllm/pull/39983
• @sphinx07 made their first contribution in https://github.com/vllm-project/vllm/pull/43817
• @SunskyXH made their first contribution in https://github.com/vllm-project/vllm/pull/44215
• @ThibaultCastells made their first contribution in https://github.com/vllm-project/vllm/pull/43636
• @tianyu-z made their first contribution in https://github.com/vllm-project/vllm/pull/43150
• @tonyliu312 made their first contribution in https://github.com/vllm-project/vllm/pull/40923
• @tushar00jain made their first contribution in https://github.com/vllm-project/vllm/pull/41980
• @viiccwen made their first contribution in https://github.com/vllm-project/vllm/pull/44617
• @Vikrantpalle made their first contribution in https://github.com/vllm-project/vllm/pull/38804
• @wanghenshui made their first contribution in https://github.com/vllm-project/vllm/pull/44410
• @willamhou made their first contribution in https://github.com/vllm-project/vllm/pull/43429
• @william-rom made their first contribution in https://github.com/vllm-project/vllm/pull/43862
• @xiaozcy made their first contribution in https://github.com/vllm-project/vllm/pull/43843
• @XuZhou26 made their first contribution in https://github.com/vllm-project/vllm/pull/44618
• @Yadan-Wei made their first contribution in https://github.com/vllm-project/vllm/pull/44559
• @zhangtao2-1 made their first contribution in https://github.com/vllm-project/vllm/pull/43175
• @zvik made their first contribution in https://github.com/vllm-project/vllm/pull/43519
• @zzt93 made their first contribution in https://github.com/vllm-project/vllm/pull/43770

Contributors

Thank you to everyone who made this release possible!

@AndreasKaratzas, @WoosukKwon, @BugenZhao, @yewentao256, @hmellor, @khluu, @njhill, @sfeng33, @bnellnm, @vadiklyutiy, @NickLucche, @JartX, @lucianommartins, @cleonard530, @wzhao18, @yma11, @simondanielsson, @jeejeelee, @zyongye, @chaunceyjiang, @bigPYJ1151, @ronensc, @taneem-ibrahim, @LucasWilkinson, @MatthewBonanni, @mmangkad, @chunyang-wen, @yzong-rh, @JaredforReal, @zixi-qi, @Isotr0py, @noooop, @chaojun-zhang, @Xunzhuo, @ivanium, @zufangzhu, @DaoyuanLi2816, @CienetStingLin, @aoshen02, @akii96, @benchislett, @MengqingCao, @rshavitt, @kliuae, @omerpaz95, @willamhou, @Majid-Taheri, @micah-wil, @ricky-chaoju, @mikekg, @mgoin, @mayuyuace, @Etelis, @ilmarkov, @tlrmchlsmth, @UranusSeven, @bedeks, @izhuhaoran, @ZJY0516, @fadara01, @pschlan-amd, @wangxiyuan, @Oxygen56, @charlifu, @varun-sundar-rabindranath, @shen-shanshan, @TheEpicDolphin, @adobrzyn, @XuZhou26, @tjtanaa, @Terrencezzj, @zhejiangxiaomai, @ILikeIneine, @yubofredwang, @chfeng-cs, @ThibaultCastells, @linzm1007, @javierdejesusda, @meenchen, @zhewenl, @xyang16, @angelayi, @nholmber, @zhangtao2-1, @adityasingh2400, @sts07142, @jatseng-ai, @fallintoplace, @andakai, @he-yufeng, @ignaciosica, @JINO-ROHIT, @tonyliu312, @QwertyJack, @animeshtrivedi, @jzakrzew, @juliendenize, @zexplorerhj, @ruocco, @mgehre-amd, @jasonboukheir, @MaciejBalaNV, @JohnQinAMD, @huanghua1994, @rajkiranjoshi, @rasmith, @harshaljanjani, @ltd0924, @wdhongtw, @yintong-lu, @tianmu-li, @jikunshang, @JMonde, @MHYangAMD, @frida-andersson, @gau-nernst, @Wauplin, @czhu-cohere, @gagandhakrey, @nemanjaudovic, @Liangliang-Ma, @liulanze, @sphinx07, @aadwived, @nightcityblade, @umut-polat, @jeffreywang88, @wcynb1023, @zzt93, @shadeMe, @Dao007forever, @alec-flowers, @Krishnachaitanyakc, @orozery, @BWAAEEEK, @cinnamonica02, @albertoperdomo2, @Rukhaiya2004, @mfylcek, @shreyas269, @Gruner-atero, @TomerBN-Nvidia, @wjinxu, @IdoAtadTD, @xiaozcy, @brian-dellabetta, @zhenwei-intel, @adotdad, @Kartavyasonar, @lesj0610, @ECMGit, @cakeng, @william-rom, @qiching, @NolanHo, @andylolu2, @xwu-intel, @linitra24, @hoobnn, @Dymasik, @wanghenshui, @maobaolong, @oguzhankir, @Jie-Fang, @okorzh-amd, @Kevin-XiongC, @jiahanc, @garrygale, @dsikka, @QiliangCui2023, @wjabbour, @zvik, @tc-mb, @jwzheng96, @divakar-amd, @tushar00jain, @galletas1712, @hanlin12-AMD, @tuukkjs, @viiccwen, @Sunt-ing, @HueCodes, @tianyu-z, @adhithyamulticoreware, @rishitdholakia13, @effi-ofer, @Vikrantpalle, @walterbm, @devin-lai, @Yadan-Wei, @amd-fuweiy, @maeehart, @qyYue1389, @BramVanroy, @SunskyXH, @Holworth, @majian4work, @xaguilar-amd, @Rohan138