Releases

1. ▸▾0.30.022 Sept 2026Breaking

   Breaking changes : scale-out endpoints are opt-in on plain vllm serve via --enable-scale-out , replacing VLLM_ENABLE_SCALE_OUT_ENDPOINTS ( #54579 , #5…

   v0.30.0

   Highlights

   This release features 762 commits from 315 contributors (104 new)!

   • New models: DeepSeek-V4.1-Flash (#56214, #56228, #56208) with the whole KV stored in MXFP8 through the FlashMLA V4.1 record on SM100 (#56893), DeepGEMM Mega-mHC (#56962), and async Engram prefetch with Engram DP sharding (#56512); DeepSeek-V4-Flash-Vision-Exp (#54566), also on ROCm (#55107) and with LoRA (#55897); GLM-5.3-Flash (#53906) with EPLB (#55119); K2-Horizon (#55063); Cohere Compass (#54774); Bailing V3 VL (#55921); Nanbeige4.2 via the Transformers backend (#56071); and a DeepSeek-V4 CPU backend with AVX512/AMX sparse MLA, indexer, mHC and compressor kernels (#55355).
   • Fast Start: a persistent per-GPU weight-cache daemon holds post-quantized, TP-sharded weights in GPU memory so restarting engines map them over CUDA IPC with --load-format ipc_cache instead of reloading from disk (#54921), now covering FP4 checkpoints (#55465) and multi-node TP (#55468).
   • Watermarking: Gumbel-max watermarked generation and detection with a keyed PRF, per-request opt-out and an example detection endpoint (#54053); dual-key Gumbel-max makes it compatible with speculative decoding (#56122); the Rust frontend forwards the per-request controls (#56338).
   • HiSparse: a host-resident tier for sparse-MLA decode that spills KV pages to pinned host memory under GPU pressure and serves top-k misses from a per-request GPU hot buffer, enabled through HiSparseConnector (#53781), with Prometheus counters (#56061), a host cache shared across TP ranks (#56629), and the attention config inferred from the connector (#57041).
   • Model Runner V2: dual-batch overlap in eager mode (#50945) and with FULL CUDA graphs for microbatched steps (#51700); MTP (#46994) and EAGLE3/DFlash/DSpark (#50514) speculative decoding under pipeline parallelism; adaptive verification for every draft-model speculator through an online acceptance estimator (#52228); gc frozen during graph capture, cutting capture from 12s to 2s and engine init from 28.9s to 8.2s on H200 (#54646); --return-sampling-mask compacted on GPU, fixing an about 2x RL step-time regression (#54901).
   • Qwen3.8-Flash-Next performance: separate prefill and decode QSA indexer kernels (#54513), fused PLE kernels (#54517), FP8 indexer cache (#54890), padded-index skipping in sparse GQA (#54873), fused PLE residual and QSA output gate (#55309), UVA PLE offload and Engram tensor parallelism via --engram-config (#54371), and torch.compile removed from the NVIDIA implementation so FP8 fits on a single GB300 (#55272).
   • Kimi K3 performance: native CUDA AttnRes default on SM100 (#54261), KDA mixed-batch gather/scatter removed (5.2-7.7% E2E throughput, #56159), grouped FP8 MLA cache insertion (4-6x kernel speedup at small batch, #55356), DSV3 low-latency GEMM on strided tensors (12-81% kernel speedup, #54565), overlapped TP8 KDA projections (#54697), FlashInfer KDA kernels (#55364), internal prefix checkpoints with partial prefix caching and speculative decoding (#53614), and symmetric DCP disaggregation for hybrid Mamba models (#55531).
   • Large scale serving: PCP+DCP on sparse-MLA models (#56157), PCP with single-module MTP and replicated DSpark (#56107) and decode-only FULL CUDA graphs (#53867), Elastic EP reusing CUDA graphs across reconfiguration (#54985), an opt-in FlashInfer PCIe IPC all-reduce for NVLink-less boxes (#53576), DeepEP v2 async finalize overlapping shared experts with combine (#52781), Mooncake Store heterogeneous TP sharing (#53129), a KVCR secondary-tier adapter (#53624), and encoder-cache sharing over NIXL (#47941) and Mooncake (#41567).
   • Quantization: targeted online quantization through quantization_config.targets (#51285) and on partially pre-quantized checkpoints from any quant method (#51392), W4A16 DSA with the nvfp4_fp8_ds_mla KV cache (#51724), FlashInfer CuTeDSL NVFP4 W4A16 default over Marlin on SM100/103 (#53014), NVFP4 in the torch linear backend (#53319), per-quantization linear backend overrides (#51204), AutoRound 2/3/5/6/7-bit on CUDA (#52890), and DeepSelect top-k for the DSA sparse indexer (#56464).
   • Breaking changes: scale-out endpoints are opt-in on plain vllm serve via --enable-scale-out, replacing VLLM_ENABLE_SCALE_OUT_ENDPOINTS (#54579, #55176); GPTQ activation ordering (g_idx) removed (#54809); items deprecated for 0.29 removed, including the VLLM_PREFIX_CACHE_RETENTION_INTERVAL and VLLM_MM_HASHER_ALGORITHM env vars (#55353); the all Mamba cache mode deprecated (#55041); python -m vllm.entrypoints.grpc_server deprecated in favor of vllm serve --grpc (#56746); YaRN aligned with Transformers so vendor YaRN aliases no longer re-scale max_model_len (#56446).

   Release Artifacts

   Python Wheels

   Platform	Install
   PyPI (CUDA 13.0)	pip install vllm
   PyPI (CUDA 13.0, uv)	uv pip install vllm --torch-backend=auto
   ROCm	pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.30.0/rocm723
   XPU	uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.30.0/xpu --extra-index-url https://download.pytorch.org/whl/xpu --index-strategy unsafe-best-match

   Docker Images

   Platform	Docker Image
   CUDA 13.0 (Default)	docker pull vllm/vllm-openai:v0.30.0
   CUDA 12.9	docker pull vllm/vllm-openai:v0.30.0-cu129
   CUDA 13.0 + Ubuntu 24.04	docker pull vllm/vllm-openai:v0.30.0-ubuntu2404
   CUDA 12.9 + Ubuntu 24.04	docker pull vllm/vllm-openai:v0.30.0-cu129-ubuntu2404
   ROCm	docker pull vllm/vllm-openai-rocm:v0.30.0
   CPU	docker pull vllm/vllm-openai-cpu:v0.30.0
   XPU	docker pull vllm/vllm-openai-xpu:v0.30.0

   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 models: DeepSeek-V4.1-Flash (#56214, #56228, #56208), DeepSeek-V4-Flash-Vision-Exp (#54566) with ROCm support (#55107) and LoRA (#55897), GLM-5.3-Flash (#53906), K2-Horizon with its reasoning and tool parsers (#55063), Cohere Compass (#54774, #55949), Bailing V3 VL with MTP (#55921), Nanbeige4.2 via the Transformers backend (#56071), and a DeepSeek-V4 CPU backend (#55355).
   • DeepSeek-V4.1-Flash: whole KV in MXFP8 through the FlashMLA V4.1 record on SM100 (#56893), Mega-mHC from the DeepGEMM fork (#56962), mHC post block folded into the delayed pre projection (#56633), Triton-fused input metadata preparation (#56562), async prefetch for CPU-offloaded Engram lookups and Engram DP sharding (#56512), DSpark draft states collapsed before the SP all-gather (#56903), DSpark drafter no longer inheriting uninitialized EPLB state (#56387), XGrammar V4.1 schema constraints for strict tool parameters (#56408), Responses text parts (#56299), and image sentinel padding scoped to the Vision-Exp checkpoint (#56554).
   • DeepSeek V4: plain RoPE without YaRN on sparse SWA layers (#54815), --kv-cache-dtype auto resolving to fp8_ds_mla with FlashMLA (#45091), prefill sparse index workspace sentinel seeded (#55299), dequant gather grid sized by rows (#55061), optional Q-norm in the fused MLA epilogue and group_size=32 packed FP8 quant (#56215), sparse settings read from the text config for composite models (#56160), MegaMoE startup without EP fixed (#55914), and a Triton iHC pre/post fallback for HY V4 (#55059).
   • GLM-5.3-Flash: EPLB support (#55119), FlashKDA for KDA chunked prefill (1.7-3.8x faster than the Triton chunk path, #55737), dense and masked-MHA sparse prefill for the NoPE (256, 0, 256) layout (#55738), decode hot-path cleanups removing per-layer copies and a duplicate router GEMM (#55736), and the glm5next NVIDIA subtree now packaged in wheels (#55214).
   • Qwen3.8-Flash-Next: separate prefill and decode QSA indexer kernels with VLLM_SPARSE_INDEXER_MAX_LOGITS_MB (#54513), fused PLE kernels and merged K/V projections (#54517), sparse GQA skipping padded indices (#54873), FP8 indexer cache (#54890), fused PLE residual and QSA output gate (#55309), UVA PLE offload and Engram tensor parallelism via --engram-config (#54371), torch.compile removed from the NVIDIA path (#55272), compact indexer logits workspace (#54915), HC combine-norm reused for MTP input (#54687), PLE MTP metadata transfers (#55054), Hopper LL-GEMM tuning table (#54560), B200 FP8 MoE tuning (#55890), and fixes for FP8 PLE weight scales (#54722, #54882) and fused PLE conv strides (#55375).
   • Kimi K3: native CUDA AttnRes default on SM100 (#54261), ll_bf16 router weight prefetch (#53524), overlapped TP8 KDA projections (#54697), strided DSV3 GEMM (#54565), MLA decode epilogue latency (#54896), NVFP4 in_proj alignment routing to DeepGEMM (#55242), mixed-batch KDA without gather/scatter (#56159), grouped FP8 MLA cache insertion (#55356), FlashInfer KDA kernels (#55364), FlashKDA bf16 checkpoint state (#56485) and a stability bump (#54859), SiTU on the FlashInfer CuTeDSL MoE backend (#54606), internal prefix checkpoints with partial prefix caching and spec decoding (#53614), and fixes for RecoverSSM startup (#54634, #55774), GDN metadata (#54636), mixed-batch masks (#54781), checkpoint indexing (#55747, #56794), KDA illegal memory access (#55924), interleaved weight streams (#53379), NVFP4 MoE conversion OOM (#55407), and KDA projection overlap on Hopper (#55426).
   • Transformers backend: attention found with a fuser and vLLM's layer attached to it (#54941), generic merged-column linear fusion (#55301), QKV fuser for Gemma4 (#55690), all vocab embeddings replaced including Gemma 3n/4 per-layer tables (#54760, #55083), and attention sinks applied (#52156).
   • Speculative decoding for models: EAGLE3 for Sarvam MLA (#53052), Nemotron-H MTP with a separate, possibly quantized lm_head (#54574), NVFP4 DSpark gathered top-k projection for quantized Markov heads (#55713), Qwen3.5/3.6 multimodal MTP resolving n_predict from the text config (#55369), Qwen3 DSpark padded-vocab drafts (#55133), and GLM-OCR MTP weight loading (#49869) and position masking under CUDA graphs (#56447).
   • Multimodal: torchcodec audio decoding and a selectable audio_backend in --media-io-kwargs (#51826) with automatic decoding kept soundfile-first (#55642), torchaudio as the default resampler (#52598), media_io_kwargs in multimodal hashes (#54241), cache hash kwargs scoped by modality (#54918), empty video URLs with multimodal UUIDs (#54220), cached audio inputs with UUIDs (#56310), encoder CUDA graphs for MiniCPM-V 2.5/2.6/4.0 (#42785), Voxtral Realtime with FULL_DECODE_ONLY graphs (#51167), Triton/FlashInfer composite attention for multimodal prefixes (#56305), pruned sliding-window tiles for Gemma 4 multimodal prompts (up to 3-4x E2E, #53147), SDPA for BLIP-2 Q-Former (#55285), baddbmm Conformer scores (#55062), fused DeepEncoder relative bias (#55629), Gemma3n sparse GELU Triton kernel (#48498), and no duplicate text embedding in Qwen2.5-Omni (#55415).
   • Correctness: Gemma 4 / Gemma 3n fine-tuned checkpoints without KV projections on shared layers (#54917, #55864), Gemma4 video preprocessing without image kwargs (#56652) and unified audio profiling (#56721), MiniCPM-V 4.6 loading (#49417) and video embeds (#54633), InternVL video parser (#55779), Nano-Nemotron precomputed embeddings (#56398), OpenPangu multimodal merge (#55941) and sleep mode (#53696), Qwen3-VL and Cosmos3-Edge under CPU and PP (#53699, #43272), Qwen VL processors without a tokenizer (#54886), Molmo2 fps guard (#55893), CohereASR audio-token estimate (

   Note truncated.