v0.31.0

Highlights

This release features 717 commits from 307 contributors (96 new)!

• DeepSeek-V4.1-Flash performance: FlashMLA mega attention with the V4.1 NVFP4 compressed KV cache is now the SM100 default (#56935); DeepGEMM sparse MQA logits for the indexer (#56254) and Mega-Gate fusing the gate GEMM with expert selection (#56266); decoder boundaries fuse the TP all-reduce, mHC input preparation (#57643) and the MoE finalize (#58586); a fused small-batch WO-A with inverse RoPE and MXFP8 quant on SM100/SM103 (#58634); the MXFP8 wo_b GEMM fused with the sequence-parallel reduce-scatter (#57428); Engram wkv sharded across TP ranks (#58678) and Engram host tables shared across co-located DP replicas by default (#57651); encoder CUDA graphs for the vision tower (#56625); and SWA bounded replay that keeps the sliding-window KV out of prefix caching (#56227).
• Fast restart: the new vllm preload CLI launches the weight-cache daemon that keeps post-quantized weights resident in GPU memory across engine restarts (#56680), now with data parallelism (#57386), MTP draft models (#57312), a /health endpoint (#58552) and a readiness wait (#58370). Experimental initialized-engine snapshots (vllm snapshot create/restore) use CRIU to restore a fully initialized TP1 engine (#51360).
• Model Runner V2 and speculative decoding: draft-model speculative decoding (#43091) and custom logits processors (#56497) on Model Runner V2; the new LiLiCorr drafter (#57934); async scheduling for DFlash (#58065) with the context K/V precompute captured in the draft CUDA graph (#57632); DSpark adaptive verification for Gemma4 (#57263) and variable-length decode for Kimi-K3 (#52988); a DCP target with a non-DCP DSpark draft (#56723); and MoE memory now counted during MRV2 profiling, avoiding OOMs on WideEP deployments (#57270, #58411).
• Large scale serving: MoonEP balanced EP all2all backend via --all2all-backend moonep (#52101), prefill context parallelism with data parallelism (#57075), a low-SM multimem reduce-scatter for SM100/SM103 (#55072), DeepEPv2 with sequence parallelism (#57210) and EPLB with shared-expert overlap (#57236), a sharding-aware NCCL M2N weight-transfer backend for RL (#51520), and KV offloading back-pressure detection (#50045).
• Scheduling controls: --max-num-active-seqs caps RUNNING admission independently of max_num_seqs (#56758), --long-prefill-token-threshold now adapts to the number of waiting prefills instead of chunking a lone request (#57951, #58459), the waiting queue was reworked so requests already holding KV blocks are scheduled first (#58947), and the KV connector + MTP deadlock under KV pressure was fixed (#57104).
• HiSparse hardening: MTP verification rows resolved with a union residency kernel (#59235), MTP acceptance collapse under FULL graphs fixed (#59309), no GPU pages without host backing (#59036), a chunked-prefill preemption livelock fixed (#59494), KV cache sized from the groups HiSparse allocates (#59450), and host prefix publication and GPU prefix adoption fixes (#59007, #59282).
• Security: per-request mm_processor_kwargs and media_io_kwargs are rejected unless --trust-request-mm-kwargs is set (#58830); prefix-cache extra keys are tagged by source so a LoRA name and a cache_salt can no longer collide (#51899), and the LoRA path is part of the block hash (#59335); stale multimodal receiver-cache entries can no longer replace fresh payloads (#57833).
• Breaking changes: per-request multimodal kwargs gated (#58830); tokenizer_mode="slow" removed (#58545); --enable-mamba-fine-grained-prefix-cache renamed to --enable-mamba-shared-prefix-checkpoint (#57382); online quantization through quantization="fp8" replaced by the fp8_per_tensor shorthand (#53585) and Quark silent online quantization removed (#51800); the AllSpark INT8 W8A16 backend removed (#58001); --enforce-eager now also disables JIT kernel warmup (#58197); XPU graphs enabled by default with VLLM_XPU_ENABLE_XPU_GRAPH removed (#51600).

Release Artifacts

Python Wheels

Docker Images

Other Artifacts

Pre-built release artifacts are available in the Assets section at the bottom of this page, including: - Source distribution tarball - CUDA 12.9 Python wheels for x86_64 and arm64 - CUDA 13.0 Python wheels for x86_64 and arm64 - CPU Python wheels for x86_64, arm64, and macOS - XPU Python wheel for x86_64

Model Support

• New model capabilities: DiffusionGemma structured generation mode with bounded single-token choices (#57250), MiMo V2 MXFP4 MoE, BF16 MoE router and DFlash drafts (#57784), Cohere2MoE auxiliary hidden states for EAGLE3/DFlash drafters (#49819), GLM-5.2-MXFP4 on the ROCm DeepSeek-V3.2 path (#51915), AMD-Quark mixed-precision DeepSeek-V4.1-Flash-MXFP4 (#57071) and GLM-5.3-Flash Quark MXFP4 (#56176) checkpoints, and a built-in granite_thinking_parser for Granite 4.2 (#55957).
• DeepSeek-V4.1-Flash: FlashMLA mega attention with the NVFP4 compressed KV cache as the SM100 default (#56935), DeepGEMM sparse MQA logits in the indexer (#56254), Mega-Gate (#56266), fused TP all-reduce + mHC input preparation (#57643) with the MoE finalize folded in (#58586), fused small-batch WO-A (#58634), MXFP8 wo_b GEMM + reduce-scatter (#57428), overlapped mHC coefficients for small TP batches (#57603) limited to FULL CUDA graphs (#57874), Engram wkv sharded across TP (#58678), serialized Engram lookups with huge-page host tables (#56926), Engram tables shared across DP replicas by default (#57651, #57914, #59068), native shared-expert MegaMoE fusion without padding (#56568, #57204), faster MegaMoE staging and NVFP4 cache gathers (#57604), the fused query RMSNorm + MXFP8 path restored (#57679), KV-only DSpark context insertion (#56441), vision encoder CUDA graphs (#56625, #58499), SWA bounded replay (#56227), causal image SWA restored to match the reference (#57152), updated reasoning effort mappings (#58316), and startup fixes for NaN-scored candidate blocks (#57454), DeepSelect sentinels (#58215), DSpark non-causal attention on FlashInfer (#57432), runtime JIT of offset candidate buffers (#57667) and imports without Triton (#57654).
• DeepSeek V4: FlashInfer sparse MLA with fused inverse RoPE + FP8 quant (#58621), stacked DSpark context WKV projections (#54674), fused MoE expert distribution computed from the EP group (#57465), FIM completion via suffix (#44229), missing string= in tool calls parsed (#56271), request tools attached to an existing system message (#51856), image block spacing preserved (#56882), and DSpark width separated from MTP stage validation (#54631).
• GLM-5.3-Flash: opt-in FlashAttention and FlashMLA sparse backends on SM90 (#55385), NoPE sparse MLA on the FlashInfer SM120 backend (#55277), cooperative top-k for small decode batches (#57327), indexer decode workspace sized by pooled length (3 GiB saved, #57701), metadata ops 1.6-4.8x faster (#58450), fused kpool tail slot mapping (#57534), kpool top-k through the shared dispatcher (#57546), lower sparse MLA preparation overhead (#57458), no D2H sync in the SM90 sparse MLA plan under async scheduling (#58684), FlashKDA keeping the recurrent state in FP32 for long prefills (#58846), and correctness fixes for kpool corruption with speculative decoding (#58454), SM90 index_kpool mismatch (#58704), kpool tail strides (#57477), 500k-token prompts (#57317), dense MLP layers under sequence parallelism (#58061), indexer top-k backend selection (#58594) and varlen paged MQA launches (#55270).
• Qwen3.8-Flash-Next (Qwen4Exp): FP8 main KV cache on the QSA path (#55557), FP8 TP with FlashInfer TRTLLM MoE (#55867), SM90 QSA tuning (#57273), fused HC down projection + SiLU (#58957), lower PLE metadata overhead (#58114), QSA indexer workspace fragmentation fixed (#57105), profiling KV cache released (#58961), pinned PLE prefetch ids kept out of the CUDA graph pool (#58489), and indexed expert-mapping lookups saving about 25 s of weight loading on DGX Spark (#58720).
• Kimi K3 and MiniMax-M3: Kimi-K3 vision patch embedder as a GEMM (#58527), fused KimiViT QK RoPE (up to 29x, #58651), routed expert quantization (#57430), reasoning parser (#57098) and reasoning token counting (#58372) fixes, DSpark context KV pointers refreshed after re-binding (#58814), stateless first chunks no longer classified as decodes (#51483), and Mamba block estimates that no longer stall admission on external prefix hits (#57050); MiniMax-M3 encoder CUDA graphs (#58673), Conv3dLayer patch embedding (about 62x, #58512), triton_mrope in the vision tower (#58526), fused MiniMax2 routing with non-unit scaling (#58880), and processor fixes (#58460, #59613).
• DiffusionGemma: one-pass sampler statistics kernel (#58226), diffusion_constrained reads over logprob_token_ids (about 25% faster, #58216), fewer logit rows for prefill-only batches (#57416), logprob_token_ids support (#57417), and fixes for concurrent logprobs (#57414), eager fallback dtype (#57462), multimodal inputs (#57589), quantized LM heads (#48521) and CPU execution (#58964).
• Multimodal: Triton mm_input_norm kernel (#56798, #56711), raw pixels kept through the DP-sharded ViT path (#56872), Qwen2.5-VL video fps honored for temporal M-RoPE (#47736), Whisper and Qwen2-Audio clips longer than 30s (#57769, #56912), Mistral3 placeholder grid (#53758), Idefics3 unsplit patches (#48760), Ovis2.5 tokens (#52623), Aria expert weights (#57487), MiMo-V2.5 fused FP8 qkv_proj sharding (#57508), Gemma4 AutoWeightsLoader (#55911) and buffer scalars (#54213), Gemma4 FP8 KV with FA4 at head dim 512 (#53175), Laguna RoPE (#57189), Mistral-Large-3 accuracy regression (#57563), malformed EXIF (#56527, #57234), JinaVL labels (#57347), Sarvam MLA routing with FP32 router logits (#56034), fused CohereASR attention scores (#55190), and fused DFlash2 grouped convolution (#55960).
• LoRA: Nemotron VL language models (#56231), ModernBert (#57148), VoyageQwen3 embeddings (#57708), RoBERTa sequence classification (#58884), and modules_to_save sequence-classification heads (#53555) with per-adapter num_labels (#57766).

Engine Core

• Model Runner V2: draft-model speculative decoding (#43091), custom logits processors (#56497) validated at admission (#57728), randomized dummy inputs (#58411), dummy tokens routed to MoE experts during profiling (#57270), FULL decode graphs for one-token prompt tails (#58400), token-to-request mappings shared (#57102) and Mamba/GDN metadata reused across KV cache groups (#58762), encoder-only ViT CUDA graphs (#56922), weight offloader (#57834) and pooling model (#57737) released on shutdown, and fixes for multi-layer MTP KV in P/D (#55055), fast-prefill with LoRA (#56456), stale block-table writes from dummy draft steps (#56734), padded prompt tails in hybrid models (#58434), never-proposed draft slots (#58784), auto-fit max_model_len (#58149) and intermediate_tensors during capture (#57745).
• Speculative decoding: LiLiCorr drafter (#57934), DFlash async scheduling (#58065) and context K/V in the draft CUDA graph (#57632), Gemma4 DSpark adaptive verification (#57263), Kimi-K3 variable-length adaptive verification (#52988), CPU-GPU sync removed for heterogeneous vocabularies (#57396), Triton recompiles avoided in the acceptance estimator (#57107), and fixes for EAGLE/dense drafts with EP (#56930), DFlash/DSpark profiling batches (#56448), GLM MTP head memory (#55442), prompt embeddings with drafts (#57356), and TritonMLA causal multi-token decode (#51065).
• Scheduler: --max-num-active-seqs (#56758), adaptive --long-prefill-token-threshold (#57951, #58459), skipped_waiting replaced by a KV-holding waiting queue (#58947), atomic admission of n > 1 requests (#53936), KV connector + MTP deadlock (#57104), a throughput cliff when max_num_seqs is not a multiple of 8 (#57355), streaming continuations keeping logprobs and refreshing max tokens (#57447, #57676), a resumable request + async scheduling race (#58259), and a non-blocking structured-output grammar poll (#55931).
• Prefix caching and hybrid models: extra keys tagged by source (#51899) and LoRA paths hashed (#59335), --enable-mamba-shared-prefix-checkpoint (#57382), prompt-tail hits with MTP restored (#58368), prompt-end checkpoints kept under sparse retention (#59146), align-mode checkpoint reservation (#59175), stateless GDN first chunks (#51565), MTP draft KV cache groups annotated on the hybrid grouping path (#55390), a generalized prefill checkpoint builder (#57783), batched Mamba2 prefill state saves without GPU-CPU syncs (#49371), skip_reading_prefix_cache honored for connector hits (#57269), incremental multimodal block hashing (#51694), a KV block size every attention backend supports (#49845) with clearer errors (#58557), and CacheConfig.effective_attention_block_size for DCP (#56538).
• Startup and memory: parallel Triton warmup compilation (#58582), JIT warmup disabled under --enforce-eager (#58197) unless fault tolerance is on (#58593), DeepGEMM warmup reusing the MoE workspace (#57268), allocator fragmentation no longer shrinking the KV cache during profiling (#58430), sparse prefill buffers reserved before KV sizing (#57575), stale FlashMLA workspace views released (#56902), DeepGEMM FP8 workspace halved (#53914), shared Marlin/Humming workspaces (#57421), FlashInfer BF16 MoE weights converted in place (#54699), UniProc startup threads bounded by the CPU quota (#58946), and runtime threads set before profiling (#55891).
• Kernels: sampled filtering for persistent top-k (#56346), Murmur3 RNG for Gumbel sampling (#51367), register-resident per-token-group 8-bit quant (#55330), vectorized per-tensor FP8 abs-max (#58194), a Triton kernel dispatcher for platform-specific overrides (#43048), GateLinear for all MoE models (#58234), non-local expert slots skipped in TritonExperts (#58051), deferred TRT-LLM-Gen top-k finalize on the modular path (#58635), SM120 batch-invariant matmul configs (#57456), FlashInfer prefill dequant scratch bounded (#57918), and fixes for Triton softcap NaNs (#56579), QuIP Hadamard transforms (#43462), CUTLASS FP8 linear on A100 (#55884), FlashInfer sampling on unsupported GPUs (#48956), SM100 FP8 blockwise scale padding (#57377), mixed FULL graph prefill capture (#58275) Inductor custom-op pattern matching (#58189), CuteDSL BF16 GDN prefill diverging from FLA on Qwen3.5 (#53864), SM100 fp8_ds_mla cache scales (#49435), ragged decode batches in the sparse indexer (#52500), stale allowed_token_ids masks after batch reordering (#48419, #43931), and prompt_embeds tensors held after requests finish (#57988).
• Batch invariance: breakable CUDA graphs without torch.compile by default under VLLM_BATCH_INVARIANT (#57586), sequence parallelism and async TP disabled (#56377), and NCCL 2.31 collectives kept enabled (#58179).
• Sleep and RL: release_kv_cache_memory() frees only KV cache memory (#44890), --sleep-preserve-parameter-names retains frozen weights across level-2 sleep (#57891), NCCL M2N weight transfer (#51520), dense DP weight updates by DP index (#56950), allocator config kept when toggling expandable segments (#57982), and cache reset failures propagated during sleep (#54581).
• Fast restart: vllm preload (#56680), DP (#57386), MTP drafts (#57312), readiness wait (#58370), /health (#58552), ModelOpt MXFP8 pre-processed weights (#57316), and initialized-engine snapshots (#51360).
• Logging and observability: LoggingConfig via --logging-config and --log-level (#57205), JSON logging fixes (#57957, #58747), startup log suppression fixed (#51366), unified platform-aware torch profiling (#57460), no 0.0% prefix cache hit rate before any query (#54990), KV transfer metrics formatting (#57068), MFU activation sizing from the model dtype (#57070), platform-overridable env var checks (#48599), VLLM_TARGET_DEVICE=empty pip install vllm for out-of-tree backends (#41074), and the correct vLLM version reported when installing from source (#57295, #57744).

Large Scale Serving

• MoE communication: MoonEP backend (#52101), DeepEPv2 with sequence parallelism (#57210) and EPLB + shared-expert overlap (#57236), low-SM multimem reduce-scatter (#55072), EPLB load statistics during Elastic EP scaling (#58473), SP padded rows skipped in grouped routing so rank 0 is no longer a prefill straggler (#56079), hash routing rejected on unsupported monolithic backends (#57867), sampled-token broadcasts skipped under PP for requests leaving the engine (#58542), DBO with DeepEP low-latency profiling (#57502), external LB with replicas sharing nodes (#53743), and no blocking RPC during the engine handshake (#57226) or unbounded draft-token waits (#58779).
• Context parallelism: PCP with DP, EP and MTP (#57075), a DCP target with non-DCP DSpark/DFlash drafts (#56723), DCP sequence lengths without a CPU-GPU sync (#58169), and NIXL DCP pulls across MLA cache regions (#57389).
• KV connectors: NIXL pipeline-parallel push prefill for attention-HMA (#50494) and packed MLA layouts (#50499), transport-failure metrics split from KV expiry (#55854), dead peer state released without waiting for TTL (#50047), expired leases reaped behind a heartbeated head (#58292), push completion restored (#58188), D-side activity recorded (#52245); Mooncake CUSTOM_MEM_POOL (#49300), request-level load failures under HMA (#56855, #57174) and bootstrap retries (#58919); MoRIIO hybrid Mamba/KDA state in READ mode (#51052) and a multi-decode routing race (#51681); prefill cache hits in prompt_tokens_details (#54222); KV-event publishers bound at port 0 (#55844); KV cache metadata GET restored for external consumers (#56925); partial-block KV events keep every multimodal feature (#58288); saves finalized on steps without a forward (#57775); abort-safe ExampleHiddenStatesConnector (#56841); DecodeBench FP8 fills (#58472); a KvHints request envelope (#53423); and the AuxOutput connector for routed-expert outputs (#45635, #58150, #58205).
• KV offloading: per-request max_load_tokens (#55885), back-pressure detection (#50045), SimpleCPUOffloadConnector Prometheus metrics (#57251), non-prefix-cacheable (#56810) and scratch (#57145) groups skipped, replicated layouts for multi-group MLA (#57652), regions of 512 GiB or more registered in chunks (#51081), cgroup memory checked before SHM allocation (#54014), and fixes for cache recency (#51787), MTP-retained sliding windows (#56709), canonical MLA rows (#56799), event metadata (#57453) and ROCm pinned memory (#57160).
• HiSparse: union residency kernel for MTP rows (#59235), MTP acceptance under FULL graphs (#59309), host-backed allocation (#59036), preemption livelock (#59494), KV cache sizing (#59450), host prefix publication (#59007), GPU prefix adoption (#59282), and residency metrics (#58725).
• Encoder disaggregation (EPD): dynamic EPD proxy with launcher-managed registration (#54176), image requests batched per encoder (#57095), cross-encoder caching through Mooncake (#56242), metadata-only audio inputs (#57887), language-model shards skipped for --mm-encoder-only (#58086), EC connector metrics (#54960), and encoder-only fixes (#58490, #58287, #57696).

Hardware & Performance

• NVIDIA: FlashInfer 0.7.0.post1 (#58069, #59323), DeepGEMM pin bump with SM120 fixes (#57218), Rubin CUDA 13.4 nightly images (#55953), and QuTLASS builds with PyTorch 2.13 (#58173).
• AMD ROCm: AITER v0.1.23 (#56885, #58867), torch 2.13 and Triton 3.8 (#50605, #58006), triton_kernels 3.8 MXFP4 MoE for gpt-oss and DeepSeek-V4 (#55934), a ROCR host segfault fixed (#57328); DeepSeek-V4/V4.1 HCA dual-stream (#56853) and layer-aware CSA2 overlap (#57407), FP8 wo_a (#54894), inverse RoPE fused into the sparse decode reduce (#57435, #57451) which now emits MXFP8 for a grouped FP8 wo_a (#58456), MXFP8 GEMM on native 32x32 scales on gfx950 (#58510), reused top-k ragged metadata (#57434), faster candidate block selection (#58208), Engram tables in host memory (#57491) and Qwen3.8-Flash-Next PLE tables offloaded to host memory (#57497), an opt-in AITER ASM decode route for DCP + speculative decoding (#56861), get_top_tokens() on the DeepSeek V4 MTP drafter (#57568), DSpark adaptive verification (#52362), opt-in VLLM_DSV4_LOGITS_FIX for sparse-indexer logits on gfx950/gfx942 (#50455), an accuracy revert of #56433 and #51692 (#57132), and clear errors for FSE with DPA+ETP (#57919); Kimi-K3 a4w4 FlyDSL kernels (#53940) with VLLM_ROCM_USE_AITER_MOE_SITUV2=a16w4|a8w4|a4w4 (#58201), low-concurrency speculative KDA (#58045), sharded latent MoE under EP (#54956) and fewer projection copies (#50592); a ROCm Hy4 path with torch.compile (13x lower decode latency, #57526); MiniMax-M3 AITER QK-norm fusion (#54535), copy-free K/V insert (#56849) and MXFP8 fixes (#53674, #58089); GLM-5.3-Flash boot fixes (#57192, #57252, #57425); AITER QuickReduce + RMSNorm (#48249), BF16 AsyncTP (#58098), static FP8 attention output fusion (#58099), QK-norm/RoPE/KV-cache fusion for MRoPE (#50212), AITER GDN decode for flat layouts (#53623), wvSplitK for single-output GEMMs (#53283), 69 fewer copies per decode step on the skinny GEMM path (#58566), tuned GEMM lookup through AITER (#55001), a narrower Triton prefill KV tile on RDNA3/RDNA4 (#58225), staged large pageable H2D copies (#56343), and fixes for MXFP4 MoE padding starving the KV cache (#56359), AITER MLA FP8 prefill OOM (#57923), unquantized cache descales (#56726) and AITER MoE fallbacks (#56590, #57866, #57426). VLLM_ROCM_USE_AITER_FP4_ASM_GEMM is restored and off by default (#57055); SWA bounded replay is disabled on ROCm (#57906).
• Intel XPU: PyTorch 2.14 (#56013), XPU graphs on by default (#51600), EPLB (#44987), int8 W8A8 MoE on Triton (#53162), batch invariance (#55881), SYCL rotary embedding (#55721), fused QK RMSNorm + RoPE + gate (decode region 55% faster, #56096), Model Runner V2 sampler (#57277) and PP microbatch control (#55145), --device-ids honored (#56015), and device pointer overflow fixed (#54514).
• CPU: FP8 W8A8 linear and MoE for Intel Diamond Rapids (#49942), Arm paged attention up to 25% faster (#56045), Zen DA8W4 int4 for dense and MoE layers (#54024), zentorch SDPA for encoder attention (#54508) and MLA prefill (#54967), FP32 attention sinks (#56252), W8A8 INT8 MoE on POWER (#55316), W4A16 Whisper (#58268), wheels built on Ubuntu 22.04 (glibc 2.34) with AMX-FP8 (#58515), AVX10.2 gated on compiler support (#58133), pre-built Triton CPU (#58140), --device-memory-utilization alias (#56547), vLLM Recipes in the CPU image (#58796, #57306), s390x protobuf pin (#54978) and torchcodec video (#58693), and fixes for Ministral FP8 (#56985), FP32 router weights (#56168), zentorch import failures (#54923), macOS multimodal SHM (#57142), CPU affinity per local rank (#53636) and NIXL GDN state layout (#53300).

Quantization

• Humming: Hadamard transforms and NVFP4/MXFP4/MXFP8 online quantization (#56685), asymmetric wNaM through compressed-tensors (#46528), humming-kernels 0.1.16 (#58054), and Humming in the W4A8 (INT4xFP8) MoE oracle (#58427).
• New capabilities: native Quark W4A16 INT4/UINT4 exports (#48606), opt-in load-time MXFP4 dequantization (#50814), explicit per-token NVFP4 MoE backends (#57176), and a canonical N-first layout for compressed-tensors WNA16 MoE (#52798).
• Fixes: MXFP8 on layers below mm_mxfp8 shape limits (#54223), online NVFP4 scales on reload (#57954), LM head linear metadata (#58444), and the fused SiLU-mul block-quant path skipped under a SwiGLU clamp (#57984).

API & Frontend

• New options and endpoints: --tool-strict-level (#56268), response_format with tool_choice=auto (#56086), DeepSeek-V4 FIM completions (#44229), release_kv_cache_memory() and POST /release_kv_cache_memory (#44890), fixed-token prefill scoring via prompt_logprob_token_ids (#54335), per-request speculative decoding metrics in /inference/v1/generate (#43310), per-request metrics (#55084) and cache_write_tokens (#57222) in the Responses API, Anthropic thinking in /v1/messages (#58613), streaming reasoning and tool calls from the derender endpoint (#50550) with offloaded detokenization (#57528), vllm chat streaming thinking output (#57045), request body debug logging with --enable-log-requests (#58163), and only the summary line of config docstrings in --help (#57357).
• Structured output and parsers: native Lark grammars in the xgrammar backend (#58321), XGrammar 0.2.7 (#57272), Granite migrated to the streaming Parser Engine (#49648), and fixes for reasoning boundaries (#56635), xgrammar choices with control characters (#48115), list-typed JSON Schema (#48416), empty structural_tag (#47450), outlines EOS handling (#58612, #57743), parser-suppressed streaming logprobs (#58583), Inkling tool names after reasoning (#58792), and length finish_reason for truncated streaming tool calls (#46303).
• OpenAI, Anthropic and Harmony compatibility: reasoning token counts for Harmony, DeepSeek-V3 and Step3 (#58626) and per Responses tool round (#58927), Harmony max_output_tokens in the tool loop (#58551), batched chat completions using the adjusted requests (#58929, #58958), a fresh parser per choice (#58939), deferred reasoning recounts (#56067), Responses MCP cleanup (#56988) and image detail default (#57241), Anthropic inline system detection (#58754) and disabled thinking with P/D (#58786), stop strings rejected on --tokens-only servers (#57058), invalid prompt_embeds returning 400 (#55451, #57006), prompts bounded after multimodal expansion (#57076), --override-generation-config penalties (#50769), Hub revisions (#56092, #57461), full logprobs in token-in/token-out responses (#58488), generative scoring cancellation (#57729, #58788), gRPC keepalive pings (#55102), and run-batch diarized transcriptions (#57948).
• Pooling: chunked embedding padding (#56505) and normalization (#57498), reranker tokenization with document limits (#57666), and BERT-family heads kept for raw logits (#57664).
• Rust frontend: --hf-overrides (#56931), --sse-keep-alive-interval (#58306), custom chat roles (#58311), MiMo V2.5 parsers (#57933), normalized reasoning controls (#56998), parser-owned output grammars (#55269, #57340), sampling masks over gRPC (#56777), local DP size in gRPC metadata with vllm-proto 0.3.0 (#57116, #57233), a per-request preemption histogram (#57033), lock-free histograms (#58574), Nemotron-H vision context (#57634), model-owned vision processors (#58109), an mm-processor benchmark (#51922, #58084, #58378), unsupported serve args recognized (#58330), NaN logprobs no longer killing the engine client (#51026), appended EngineCoreOutput fields accepted (#56533), and vllm-rs on PATH in the CUDA image (#57606).
• Benchmarks: an openai-responses backend for vllm bench serve (#54628) and model_id in latency/throughput JSON (#58112).

Security

• Per-request mm_processor_kwargs and media_io_kwargs are rejected by default; trusted deployments opt in with --trust-request-mm-kwargs (#58830).
• Prefix-cache block hashes tag extra keys by source (#51899) and include the LoRA path (#59335); multimodal hash input is framed (#54283) and incremental block hashing covers every overlapping feature (#51694).
• Fresh multimodal payloads take precedence over a stale receiver cache (#57833), and encoder-cache hits with mismatched embedding counts are rejected (#57696).
• min_tokens above the filled max_tokens default is rejected instead of wedging the engine (#57731); message sanitization filters upper-case memory addresses (#58832); LoRA adapters named after a served model are rejected (#59286).

Dependencies

• FlashInfer 0.7.0.post1 (#58069, #59323), Transformers 5.17.0 (#56108) with an upper bound in requirements (#59614), XGrammar 0.2.7 (#57272), oss-harmony replacing openai-harmony (#55128), DeepGEMM fork pin bump (#57218), FlashKDA bump (#58846), and humming-kernels 0.1.16 (#58054).
• CUDA 12 images use LMCache 0.4.4 and CuPy CUDA 12 (#57945); a separately tagged zstd Docker Hub image (-x86_64-zstd) is published (#55608); Rubin CUDA 13.4 nightly images (#55953).
• ROCm: AITER v0.1.23 (#58867), torch 2.13, Triton 3.8 (#50605, #58006), patched ROCR (#57328), LMCache OpenTelemetry pins (#59056).
• XPU: PyTorch 2.14 (#56013). CPU: wheels built on Ubuntu 22.04 (#58515), pre-built Triton CPU (#58140).

Breaking Changes & Deprecations

• Per-request mm_processor_kwargs and media_io_kwargs now return an error unless the server is started with --trust-request-mm-kwargs; server-level --mm-processor-kwargs / --media-io-kwargs and offline LLM are unchanged (#58830).
• tokenizer_mode="slow" was removed; it already behaved like "hf" under Transformers v5 (#58545).
• --enable-mamba-fine-grained-prefix-cache was renamed to --enable-mamba-shared-prefix-checkpoint (#57382).
• Online quantization through quantization="fp8" now redirects to the fp8_per_tensor online shorthand (#53585); Quark-specific silent online MXFP4 quantization was removed in favor of the online quantization API (#51800).
• The AllSpark INT8 W8A16 GEMM backend was removed (#58001). The return_assistant_tokens_mask option of /render and the assistant_tokens_mask response field were removed (#57520).
• VLLM_PLE_CPU_OFFLOAD was removed; use --engram-config (#57937). VLLM_XPU_ENABLE_XPU_GRAPH was removed and XPU graphs are on by default (#51600).
• The comma-separated form of --collect-detailed-traces was removed; use the list syntax (#55702).
• New defaults: --enforce-eager also disables JIT kernel warmup unless fault tolerance is enabled (#58197, #58593); VLLM_BATCH_INVARIANT=1 uses breakable CUDA graphs without torch.compile (#57586) and disables sequence parallelism and async TP (#56377); DeepSeek-V4.1 SWA bounded replay is on (#56227) except on ROCm (#57906); Engram host tables are shared across co-located DP replicas when possible (#57651); FlashMLA mega attention is the DeepSeek-V4.1 default on SM100 (#56935); the AITER w4a4 ASM GEMM is off by default on ROCm (#57055).
• Model Runner V1 + PP > 1 + async scheduling + structured output is now rejected at startup (#56250); json_object is rejected at validation with the outlines backend (#57743).
• transformers now has an upper bound in requirements (#59614).

New Contributors

• @0z5a made their first contribution in https://github.com/vllm-project/vllm/pull/56441
• @200lz made their first contribution in https://github.com/vllm-project/vllm/pull/55528
• @AARONKANG04 made their first contribution in https://github.com/vllm-project/vllm/pull/48521
• @acsoto made their first contribution in https://github.com/vllm-project/vllm/pull/57314
• @adenzhou1350 made their first contribution in https://github.com/vllm-project/vllm/pull/56882
• @adtygan made their first contribution in https://github.com/vllm-project/vllm/pull/56325
• @amasen02 made their first contribution in https://github.com/vllm-project/vllm/pull/56576
• @amd-sriram made their first contribution in https://github.com/vllm-project/vllm/pull/53792
• @andrewor14 made their first contribution in https://github.com/vllm-project/vllm/pull/52956
• @Ankit-Jaiswal-AMD made their first contribution in https://github.com/vllm-project/vllm/pull/56252
• @baljinderhothi-cohere made their first contribution in https://github.com/vllm-project/vllm/pull/58792
• @BaoYunkai made their first contribution in https://github.com/vllm-project/vllm/pull/57568
• @blipbyte made their first contribution in https://github.com/vllm-project/vllm/pull/55612
• @BPbruce made their first contribution in https://github.com/vllm-project/vllm/pull/46466
• @chuan932 made their first contribution in https://github.com/vllm-project/vllm/pull/55330
• @coderfornow made their first contribution in https://github.com/vllm-project/vllm/pull/54978
• @CZT0 made their first contribution in https://github.com/vllm-project/vllm/pull/51694
• @devtyagi3909 made their first contribution in https://github.com/vllm-project/vllm/pull/56635
• @dilberx made their first contribution in https://github.com/vllm-project/vllm/pull/57076
• @divyvasal made their first contribution in https://github.com/vllm-project/vllm/pull/49845
• @equiluxe made their first contribution in https://github.com/vllm-project/vllm/pull/48115
• @errmakov made their first contribution in https://github.com/vllm-project/vllm/pull/58583
• @farzad-elastix made their first contribution in https://github.com/vllm-project/vllm/pull/56168
• @freyfwt made their first contribution in https://github.com/vllm-project/vllm/pull/51367
• @garrett361 made their first contribution in https://github.com/vllm-project/vllm/pull/57984
• @git-jxj made their first contribution in https://github.com/vllm-project/vllm/pull/55702
• @gokay-ai made their first contribution in https://github.com/vllm-project/vllm/pull/58292
• @gongwei-130 made their first contribution in https://github.com/vllm-project/vllm/pull/55102
• @haosenwang1018 made their first contribution in https://github.com/vllm-project/vllm/pull/58288
• @harshit-sarvam made their first contribution in https://github.com/vllm-project/vllm/pull/56034
• @HieDean made their first contribution in https://github.com/vllm-project/vllm/pull/57356
• @huthvincent made their first contribution in https://github.com/vllm-project/vllm/pull/48419
• @i-m-aditya made their first contribution in https://github.com/vllm-project/vllm/pull/57447
• @jayzuccarelli made their first contribution in https://github.com/vllm-project/vllm/pull/58557
• @jiakangkangfuzhe made their first contribution in https://github.com/vllm-project/vllm/pull/55146
• @jiangLLM made their first contribution in https://github.com/vllm-project/vllm/pull/57487
• @jiaran-king made their first contribution in https://github.com/vllm-project/vllm/pull/56242
• @jz-yolo made their first contribution in https://github.com/vllm-project/vllm/pull/58884
• @kaijunli-infr made their first contribution in https://github.com/vllm-project/vllm/pull/52101
• @karya0 made their first contribution in https://github.com/vllm-project/vllm/pull/57453
• @KEYS-A15 made their first contribution in https://github.com/vllm-project/vllm/pull/57528
• @kwen2501 made their first contribution in https://github.com/vllm-project/vllm/pull/51520
• @laulopezreal made their first contribution in https://github.com/vllm-project/vllm/pull/57189
• @lijipeng787 made their first contribution in https://github.com/vllm-project/vllm/pull/58225
• @limitmhw made their first contribution in https://github.com/vllm-project/vllm/pull/48606
• @linnea-lin-00638949 made their first contribution in https://github.com/vllm-project/vllm/pull/47450
• @LinzeShi made their first contribution in https://github.com/vllm-project/vllm/pull/57347
• @mahird3 made their first contribution in https://github.com/vllm-project/vllm/pull/57708
• @melcheikh made their first contribution in https://github.com/vllm-project/vllm/pull/48956
• @MichaelLapshin made their first contribution in https://github.com/vllm-project/vllm/pull/57396
• @mjkvaak-amd made their first contribution in https://github.com/vllm-project/vllm/pull/48249
• @mkunredd made their first contribution in https://github.com/vllm-project/vllm/pull/58244
• @mmastrac made their first contribution in https://github.com/vllm-project/vllm/pull/57414
• @mustafayildirim made their first contribution in https://github.com/vllm-project/vllm/pull/57252
• @Navjot10 made their first contribution in https://github.com/vllm-project/vllm/pull/55390
• @olka-amd made their first contribution in https://github.com/vllm-project/vllm/pull/51065
• @PeganovAnton made their first contribution in https://github.com/vllm-project/vllm/pull/55128
• @Rakul-Chauhan made their first contribution in https://github.com/vllm-project/vllm/pull/54967
• @Ricardo-M-L made their first contribution in https://github.com/vllm-project/vllm/pull/52580
• @Ronnie-Rui made their first contribution in https://github.com/vllm-project/vllm/pull/54581
• @RyanMa29 made their first contribution in https://github.com/vllm-project/vllm/pull/57295
• @sammaji made their first contribution in https://github.com/vllm-project/vllm/pull/58626
• @sawsa307 made their first contribution in https://github.com/vllm-project/vllm/pull/56497
• @ScarWar made their first contribution in https://github.com/vllm-project/vllm/pull/49435
• @sdougbrown made their first contribution in https://github.com/vllm-project/vllm/pull/49819
• @semerandre made their first contribution in https://github.com/vllm-project/vllm/pull/55557
• @sergiofigueras made their first contribution in https://github.com/vllm-project/vllm/pull/57948
• @shallow10 made their first contribution in https://github.com/vllm-project/vllm/pull/58336
• @SIDDARTHAREDDY8 made their first contribution in https://github.com/vllm-project/vllm/pull/57743
• @simpleqt made their first contribution in https://github.com/vllm-project/vllm/pull/55936
• @tangzzycc made their first contribution in https://github.com/vllm-project/vllm/pull/53283
• @touch869 made their first contribution in https://github.com/vllm-project/vllm/pull/55844
• @tripathiarpan20 made their first contribution in https://github.com/vllm-project/vllm/pull/57140
• @twu3202 made their first contribution in https://github.com/vllm-project/vllm/pull/57769
• @ubwzwd made their first contribution in https://github.com/vllm-project/vllm/pull/55931
• @UNIDY2002 made their first contribution in https://github.com/vllm-project/vllm/pull/58370
• @valarLip made their first contribution in https://github.com/vllm-project/vllm/pull/58659
• @vcave made their first contribution in https://github.com/vllm-project/vllm/pull/51681
• @voidxb made their first contribution in https://github.com/vllm-project/vllm/pull/49300
• @vorapolsiloai made their first contribution in https://github.com/vllm-project/vllm/pull/50212
• @vschandramourya made their first contribution in https://github.com/vllm-project/vllm/pull/58779
• @weitliao made their first contribution in https://github.com/vllm-project/vllm/pull/54535
• @wenjinhust made their first contribution in https://github.com/vllm-project/vllm/pull/51366
• @Willian-Zhang made their first contribution in https://github.com/vllm-project/vllm/pull/58720
• @wtdcode made their first contribution in https://github.com/vllm-project/vllm/pull/56268
• @xinnywinne made their first contribution in https://github.com/vllm-project/vllm/pull/55084
• @YannikHinteregger made their first contribution in https://github.com/vllm-project/vllm/pull/50047
• @YashasviChaurasia made their first contribution in https://github.com/vllm-project/vllm/pull/58112
• @Yatimai made their first contribution in https://github.com/vllm-project/vllm/pull/43462
• @YCH188 made their first contribution in https://github.com/vllm-project/vllm/pull/57190
• @yousafshah made their first contribution in https://github.com/vllm-project/vllm/pull/55957
• @ys2025-AI made their first contribution in https://github.com/vllm-project/vllm/pull/56841
• @yuchenwang3 made their first contribution in https://github.com/vllm-project/vllm/pull/54699
• @zhecfy made their first contribution in https://github.com/vllm-project/vllm/pull/58316
• @Zoe923 made their first contribution in https://github.com/vllm-project/vllm/pull/53864
• @Zyann7 made their first contribution in https://github.com/vllm-project/vllm/pull/57784

Contributors

@AndreasKaratzas, @khluu, @mgoin, @njhill, @BugenZhao, @stefankoncarevic, @robertgshaw2-redhat, @taneem-ibrahim, @Thangnguyenvn98, @hmellor, @Juntian777, @WoosukKwon, @yewentao256, @gau-nernst, @mmastrac, @LucasWilkinson, @Fangzhou-Ai, @NickLucche, @aoshen02, @JaredforReal, @mawong-amd, @DarkLight1337, @zyongye, @sfeng33, @vllm-agent, @okorzh-amd, @ZJY0516, @shen-shanshan, @gty111, @Isotr0py, @djramic, @zixi-qi, @wzhao18, @alec-flowers, @aarushjain29, @Rohan138, @yma11, @mjkvaak-amd, @ivanium, @yisustc, @MatthewBonanni, @wangxiyuan, @reidliu41, @chaojun-zhang, @shaohuaxi, @gcanlin, @ganeshr10, @linitra24, @yzong-rh, @divakar-amd, @atalman, @ShuoleiWang, @simondanielsson, @rasmith, @chaunceyjiang, @micah-wil, @liusy58, @LioEinaudi, @louie-tsai, @LiuYinfeng01, @jeejeelee, @zhenwei-intel, @jperezdealgaba, @hlin99, @zxd1997066, @lucamotz, @lucifer1004, @eopXD, @ashraf-bhuiyan, @TheEpicDolphin, @JulienDarve, @mayuyuace, @danisereb, @bigPYJ1151, @Etelis, @JohnQinAMD, @jiangkuaixue123, @tianmu-li, @akii96, @vllmellm, @RyanMa29, @afriedri, @elvircrn, @mustafayildirim, @yuzhouo7, @wtdcode, @biswapanda, @adtygan, @hickeyma, @itayalroy, @jiangLLM, @Hotragn, @faaany, @xhx1022, @jinzhen-lin, @sheralskumar, @Wauplin, @matteso1, @wjabbour, @Sunt-ing, @arpera, @hclsys, @S1ro1, @HDCharles, @fxmarty-amd, @liuzijing2014, @UNIDY2002, @samuelkim7, @markmc, @errmakov, @zhejiangxiaomai, @KernelClint, @i-m-aditya, @ColinZ22, @ppalanga, @franciscojavierarceo, @maithilijoshi20, @mfylcek, @almersawi, @albertoperdomo2, @0z5a, @jacklin78911-collab, @lzhan011, @Alex-ai-future, @freyfwt, @ZhengGong-amd, @mindungil, @amasen02, @devtyagi3909, @wenjinhust, @jbyczkow, @AdaAibaby, @harshit-sarvam, @vineethsaivs, @tripathiarpan20, @sashko-zakharchuk, @semerandre, @rebklee, @git-jxj, @cjackal, @xinnywinne, @andrewor14, @kyleliang-nv, @thillai-c, @dongluw, @ChuanLi1101, @omerpaz95, @jiaran-king, @liuyao0322, @coderfornow, @drakosha, @laulopezreal, @melcheikh, @shantipriya-amd, @Rukhaiya2004, @yuchenwang3, @lxy-alexander, @tlrmchlsmth, @HieDean, @Zoe923, @YCH188, @amd-sriram, @andylolu2, @MicheleCampi, @sdougbrown, @vMaroon, @shimib, @andakai, @thegoldenflow, @Levius-Fubuki, @olka-amd, @limitmhw, @yuwenzho, @adenzhou1350, @Josephasafg, @kylesayrs, @jdebache, @kaijunli-infr, @afierka-intel, @frida-andersson, @bnellnm, @ys2025-AI, @waizuichougou, @touch869, @rjrock, @sawsa307, @pavelzak, @ScarWar, @fadara01, @Mi-Jiazhi, @dilberx, @mahird3, @Zyann7, @roikoren755, @YukioZzz, @Ronnie-Rui, @acsoto, @czhu-cohere, @twu3202, @oliverholworthy, @weitliao, @sergiofigueras, @tuukkjs, @Sip4818, @karen-sy, @garrett361, @GirasoleY, @Navjot10, @taking-lying-flat, @wangyicong52, @majunze2001, @gangula-karthik, @ubwzwd, @linnea-lin-00638949, @kushaldabbe, @Yatimai, @guanxingithub, @jiakangkangfuzhe, @MichaelLapshin, @jhu960213, @tpopp, @mganczarenko, @qiching, @tangzzycc, @lijipeng787, @nikhilkulkarni1755, @QwertyJack, @Ankit-Jaiswal-AMD, @vorapolsiloai, @Dao007forever, @BPbruce, @divyvasal, @simpleqt, @blipbyte, @jhaotingc, @karya0, @farzad-elastix, @grYe99, @talorabr, @jackLei0901, @Yejing-Lai, @Priyjain-amd, @KEYS-A15, @LinzeShi, @bohnstingl, @nightcityblade, @vcave, @AARONKANG04, @CZT0, @snadampal, @microslaw, @haosenwang1018, @netanel-haber, @khushali9, @sammaji, @SIDDARTHAREDDY8, @100milliongold, @gongwei-130, @fululi12, @mkunredd, @mrodden, @noooop, @valarLip, @shallow10, @200lz, @askliar, @chuan932, @Monishver11, @YashasviChaurasia, @gokay-ai, @jiacao-amd, @yousafshah, @positive666, @TQCB, @lk-chen, @Ricardo-M-L, @huthvincent, @baljinderhothi-cohere, @vschandramourya, @kwen2501, @xiao-llm, @zhecfy, @Willian-Zhang, @simon-veitner-redhat, @mevince, @voidxb, @hongxiayang, @YannikHinteregger, @V-3604, @Rakul-Chauhan, @frankwang28, @LCAIZJ, @PeganovAnton, @jz-yolo, @QHarshil, @R3hankhan123, @jayzuccarelli, @yannicks1, @BaoYunkai, @xaguilar-amd, @xiaohuguo2023, @wxsIcey, @harshaladhav-amd, @varun-sundar-rabindranath, @kliuae, @LostFox11