vLLM v0.15.1 — Release Notes & Engineering Impact

Source available — automated, not human-reviewed

Component: vllm · Channel: stable. Automated analysis, not a compatibility certification. How this works.

Analysis updated: 2026-11-01.

Compatibility not verified — check source notes

$ python -m pip install 'vllm==0.15.1'

📄 .MD

⚡ Improvements 1 🔧 Bug Fixes 6

⚡ Executive Summary (TL;DR)

vLLM v0.15.1. Source highlight: v0.15.1 is a patch release with security fixes, RTX Blackwell GPU fixes support, and bug fixes.

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Who Does This Impact? (Role-Based Analysis)

💻 Software Engineers & Devs

Check the source notes against the package and APIs you use before upgrading.

📊 Product & Engineering Leads

Use the documented changes to assess relevance to your product; release frequency alone is not a quality measure.

⚙️ DevOps & Security

Verify the target version, runtime requirements and rollback procedure in the official documentation.

⚡ Performance & Enhancements (1)

**MoE forward pass**: Optimized by caching layer name computation (#33184)

🔧 Bug Fixes & Patches (6)

**RTX Blackwell (SM120)**: Fixed NVFP4 MoE kernel support for RTX Blackwell workstation GPUs. Previously, NVFP4 MoE models would fail to load on these GPUs (#33417)
**FP8 kernel selection**: Fixed FP8 CUTLASS group GEMM to properly fall back to Triton kernels on SM120 GPUs (#33285)
**Qwen3-VL-Reranker**: Fixed model loading (#33298)
**Whisper**: Fixed FlashAttention2 with full CUDA graphs (#33360)
**torch.compile cold-start**: Fixed regression that increased cold-start compilation time (Llama3-70B: ~88s → ~22s) (#33441)
Fixed prefix cache hit rate of 0% with GPT-OSS style hybrid attention models (#33524)

📄 View Raw Release Notes (Original Developer Log)

v0.15.1 is a patch release with security fixes, RTX Blackwell GPU fixes support, and bug fixes. ## Security - **CVE-2025-69223**: Updated aiohttp dependency (#33621) - **CVE-2026-0994**: Updated Protobuf dependency (#33619) ## Highlights ### Bugfix Hardware Support - **RTX Blackwell (SM120)**: Fixed NVFP4 MoE kernel support for RTX Blackwell workstation GPUs. Previously, NVFP4 MoE models would fail to load on these GPUs (#33417) - **FP8 kernel selection**: Fixed FP8 CUTLASS group GEMM to properly fall back to Triton kernels on SM120 GPUs (#33285) ### Model Support - **Step-3.5-Flash**: New model support (#33523) ### Bugfix Model Support - **Qwen3-VL-Reranker**: Fixed model loading (#33298) - **Whisper**: Fixed FlashAttention2 with full CUDA graphs (#33360) ### Performance - **torch.compile cold-start**: Fixed regression that increased cold-start compilation time (Llama3-70B: ~88s → ~22s) (#33441) - **MoE forward pass**: Optimized by caching layer name computation (#33184) ### Bug Fixes - Fixed prefix cache hit rate of 0% with GPT-OSS style hybrid attention models (#33524) - Enabled Triton MoE backend for FP8 per-tensor dynamic quantization (#33300) - Disabled unsupported Renormalize routing methods for TRTLLM per-tensor FP8 MoE (#33620) - Fixed speculative decoding metrics crash when no tokens generated (#33729) - Disabled fast MoE cold start optimization with speculative decoding (#33624) - Fixed ROCm skinny GEMM dispatch logic (#33366) ### Dependencies - Pinned LMCache >= v0.3.9 for API compatibility (#33440) ## New Contributors 🎉 * @zaristei2 made their first contribution in https://github.com/vllm-project/vllm/pull/33621 **Full Changelog**: https://github.com/vllm-project/vllm/compare/v0.15.0...v0.15.1

← Older Version vLLM v0.15.0 Newer Version → vLLM v0.16.0