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vllm A high-throughput and memory-efficient inference and serving engine for LLMs
amd blackwell cuda deepseek deepseek-v3 gpt +14
Last updated 3 days ago
© 2026 AnnounceHQ. All rights reserved.
New January 29, 2026
v0.15.0 Highlights
This release features 335 commits from 158 contributors (39 new)!
Model Support
New architectures : Kimi-K2.5 (#33131), Molmo2 (#30997), Step3vl 10B (#32329), Step1 (#32511), GLM-Lite (#31386), Eagle2.5-8B VLM (#32456).LoRA expansion : Nemotron-H (#30802), InternVL2 (#32397), MiniMax M2 (#32763).Speculative decoding : EAGLE3 for Pixtral/LlavaForConditionalGeneration (#32542), Qwen3 VL MoE (#32048), draft model support (#24322).Embeddings : BGE-M3 sparse embeddings and ColBERT embeddings (#14526).Model enhancements : Voxtral streaming architecture (#32861), SharedFusedMoE for Qwen3MoE (#32082), dynamic resolution for Nemotron Nano VL (#32121), Molmo2 vision backbone quantization (#32385).
Engine Core
Async scheduling + Pipeline Parallelism : --async-scheduling now works with pipeline parallelism (#32359).Mamba prefix caching : Block-aligned prefix caching for Mamba/hybrid models with --enable-prefix-caching --mamba-cache-mode align. Achieves ~2x speedup by caching Mamba states directly (#30877).
Session-based streaming input : New incremental input support for interactive workloads like ASR. Accepts async generators producing StreamingInput objects while maintaining KV cache alignment (#28973).
Model Runner V2 : VLM support (#32546), architecture improvements.
LoRA : Inplace loading for memory efficiency (#31326).
AOT compilation : torch.compile inductor artifacts support (#25205).
Performance : KV cache offloading redundant load prevention (#29087), FlashAttn attention/cache update separation (#25954).
Hardware & Performance
NVIDIA
Blackwell defaults : FlashInfer MLA is now the default MLA backend on Blackwell, with TRTLLM as default prefill (#32615).MoE performance : 1.2-2% E2E throughput improvement via grouped topk kernel fusion (#32058), NVFP4 small-batch decoding improvement (#30885), faster cold start for MoEs with torch.compile (#32805).FP4 kernel optimization : Up to 65% faster FP4 quantization on Blackwell (SM100F) using 256-bit loads, ~4% E2E throughput improvement (#32520).Kernel improvements : topk_sigmoid kernel for MoE routing (#31246), atomics reduce counting for SplitK skinny GEMMs (#29843), fused cat+quant for FP8 KV cache in MLA (#32950).torch.compile : SiluAndMul and QuantFP8 CustomOp compilation (#32806), Triton prefill attention performance (#32403).
AMD ROCm
MoRI EP : High-performance all2all backend for Expert Parallel (#28664).Attention improvements : Shuffle KV cache layout and assembly paged attention kernel for AiterFlashAttentionBackend (#29887).FP4 support : MLA projection GEMMs with dynamic quantization (#32238).Consumer GPU support : Flash Attention Triton backend on RDNA3/RDNA4 (#32944).
Other Platforms
TPU : Pipeline parallelism support (#28506), backend option (#32438).Intel XPU : AgRsAll2AllManager for distributed communication (#32654).CPU : NUMA-aware acceleration for TP/DP inference on ARM (#32792), PyTorch 2.10 (#32869).Whisper : torch.compile support (#30385).WSL : Platform compatibility fix for Windows Subsystem for Linux (#32749).
Quantization
MXFP4 : W4A16 support for compressed-tensors MoE models (#32285).Non-gated MoE : Quantization support with Marlin, NVFP4 CUTLASS, FP8, INT8, and compressed-tensors (#32257).Intel : Quantization Toolkit integration (#31716).FP8 KV cache : Per-tensor and per-attention-head quantization via llmcompressor (#30141).
API & Frontend
Responses API : Partial message generation (#32100), include_stop_str_in_output tuning (#32383), prompt_cache_key support (#32824).OpenAI API : skip_special_tokens configuration (#32345).Score endpoint : Flexible input formats with data_1/data_2 and queries/documents (#32577).Render endpoints : New endpoints for prompt preprocessing (#32473).Whisper API : avg_logprob and compression_ratio in verbose_json segments (#31059).Security : FIPS 140-3 compliant hash option for enterprise/government users (#32386), --ssl-ciphers CLI argument (#30937).UX improvements : Auto api_server_count based on dp_size (#32525), wheel variant auto-detection during install (#32948), custom profiler URI schemes (#32393).
Dependencies
FlashInfer v0.6.1 (#30993)
Transformers 4.57.5 (#32287)
PyTorch 2.10 for CPU backend (#32869)
DeepGEMM newer version (#32479)
Breaking Changes & Deprecations
Metrics : Removed deprecated vllm:time_per_output_token_seconds metric - use vllm:inter_token_latency_seconds instead (#32661).Environment variables : Removed deprecated environment variables (#32812).Quantization : DeepSpeedFp8 removed (#32679), RTN removed (#32697), HQQ deprecated (#32681).
Bug Fixes
Speculative decoding : Eagle draft_model_config fix (#31753).DeepSeek : DeepSeek-V3.1 + DeepGEMM incompatible scale shapes fix (#32361).Distributed : DP+MoE inference fix via CpuCommunicator (#31867), P/D with non-MoE DP fix (#33037).EPLB : Possible deadlock fix (#32418).NIXL : UCX memory leak fix by exporting UCX_MEM_MMAP_HOOK_MODE=none (#32181).Structured output : Outlines byte fallback handling fix (#31391).
New Contributors 🎉
@YunzhuLu made their first contribution in https://github.com/vllm-project/vllm/pull/32126
@emricksini-h made their first contribution in https://github.com/vllm-project/vllm/pull/30784
@dsfaccini made their first contribution in https://github.com/vllm-project/vllm/pull/32289
@ofirzaf made their first contribution in https://github.com/vllm-project/vllm/pull/32312
@seekskyworld made their first contribution in https://github.com/vllm-project/vllm/pull/32321
@brian033 made their first contribution in https://github.com/vllm-project/vllm/pull/31715
@TomerBN-Nvidia made their first contribution in https://github.com/vllm-project/vllm/pull/32257
@vanshilshah97 made their first contribution in https://github.com/vllm-project/vllm/pull/32448
@George-Polya made their first contribution in https://github.com/vllm-project/vllm/pull/32385
@T1mn made their first contribution in https://github.com/vllm-project/vllm/pull/32411
@mritunjaysharma394 made their first contribution in https://github.com/vllm-project/vllm/pull/31492
@randzero made their first contribution in https://github.com/vllm-project/vllm/pull/32511
@DemingCheng made their first contribution in https://github.com/vllm-project/vllm/pull/32556
@iboiko-habana made their first contribution in https://github.com/vllm-project/vllm/pull/32471
@honglyua-il made their first contribution in https://github.com/vllm-project/vllm/pull/32462
@hyeongyun0916 made their first contribution in https://github.com/vllm-project/vllm/pull/32473
@DanielMe made their first contribution in https://github.com/vllm-project/vllm/pull/32560
@netanel-haber made their first contribution in https://github.com/vllm-project/vllm/pull/32121
@longregen made their first contribution in https://github.com/vllm-project/vllm/pull/28784
@jasonyanwenl made their first contribution in https://github.com/vllm-project/vllm/pull/32749
@Wauplin made their first contribution in https://github.com/vllm-project/vllm/pull/32788
@ikaadil made their first contribution in https://github.com/vllm-project/vllm/pull/32775
@alexsun07 made their first contribution in https://github.com/vllm-project/vllm/pull/28664
@liranschour made their first contribution in https://github.com/vllm-project/vllm/pull/30207
@AuYang261 made their first contribution in https://github.com/vllm-project/vllm/pull/32844
@diviramon made their first contribution in https://github.com/vllm-project/vllm/pull/32393
@RishabhSaini made their first contribution in https://github.com/vllm-project/vllm/pull/32884
@MatteoFari made their first contribution in https://github.com/vllm-project/vllm/pull/32397
@peakcrosser7 made their first contribution in https://github.com/vllm-project/vllm/pull/30877
@orionr made their first contribution in https://github.com/vllm-project/vllm/pull/30443
@marksverdhei made their first contribution in https://github.com/vllm-project/vllm/pull/32614
@joninco made their first contribution in https://github.com/vllm-project/vllm/pull/32935
@monajafi-amd made their first contribution in https://github.com/vllm-project/vllm/pull/32944
@ruizcrp made their first contribution in https://github.com/vllm-project/vllm/pull/32988
@sjhddh made their first contribution in https://github.com/vllm-project/vllm/pull/32983
@HirokenOvo made their first contribution in https://github.com/vllm-project/vllm/pull/32646
@Chenhao-Guan made their first contribution in https://github.com/vllm-project/vllm/pull/32763
@joshuadeng made their first contribution in https://github.com/vllm-project/vllm/pull/28973
@ZhanqiuHu made their first contribution in https://github.com/vllm-project/vllm/pull/33016
Full Changelog : https://github.com/vllm-project/vllm/compare/v0.14.1...v0.15.0