chore(deps-dev): bump vllm from 0.22.1 to 0.29.0

[dependabot]

Bumps vllm from 0.22.1 to 0.29.0.

Release notes

Sourced from vllm's releases.

v0.29.0

Highlights

This release features 594 commits from 277 contributors (91 new)!

• Model Runner V2 is now the default for all models (#53183), completing the rollout that began with pooling models (#48290). MRV2 also gained CUDA graph memory profiling for KV cache auto-sizing (#53306), batch-sharded sampling that cuts per-step logits memory by 1/TP (#50465), prompt embeds (#42963), extract_hidden_states speculation (#49811), padded FULL cudagraph dispatch for uniform decode under spec decode (#53407), and DP-sync skipping before EAGLE/MTP draft prefill (#53694). MRV1 remains in use for a few ROCm models and features MRV2 does not yet support.
• New models: Hy4-preview, Tencent's 770B/49B-active MoE with Gated DeepSeek Sparse Attention and native MTP (#54160); Qwen3.8-Flash-Next with BF16/FP8/NVFP4 and MTP (#53896); GraniteSWA and GraniteMoeSWA (#52706); NemotronH_Omni_Reasoning_V3 with MTP (#52929, #53121); Kimi K3 NVFP4 checkpoints (#53132).
• Kimi-K3 and DeepSeek V4 performance: fused MXFP4 top-k finalization in the K3 latent tail (about 5% E2E latency, #53152), K3 Mamba metadata preparation in one Triton launch (6.6-7.6x kernel speedup, #52388), tuned Hopper low-latency GEMM (#54088) now also dispatched on SM100 (#53534) and used for eh_proj (12.9-25.2% kernel speedup, #53942), GEMM-RS extended to GEMM-AR (#53053), MLA gate merged into the QKV-A projection (#54015), K3 DCP with DSpark (#52188) and DCP partial prefix cache hits (#50493); DeepSeek V4 shared experts fused into MegaMoE (#53040), adaptive top-k width re-landed (#52823), a native SwiGLU clamp kernel for Humming MoE (#53685), and an opt-in FlashInfer moe_ep expert backend (#49636).
• Speculative decoding: per-request acceptance stats in OpenAI API responses via --per-request-spec-decode-metrics (#48915), adaptive verification extended to logprobs (#52242), SM100 sparse MLA for GLM-5.2 (#52783) and DeepSeek V4 on SM90 (#52795), Qwen3-Omni DSpark drafts (#52560), PLaMo3 EAGLE-3/DFlash (#54239), and DFlash2 loading from speculators format (#53797).
• RL weight sync: a new sharded_rdt P2P backend where each worker pulls only its TP/EP slice over NIXL or Ray Direct Transport (#43375), rank-local IPC weight updates (#52497), sparse checkpoint-coordinate updates through native weight loaders (#50723, #53751), and routed expert loading for gpt-oss (#52209).
• Mamba prefix caching: internal prefill checkpoints deliver a 9%-25% TTFT improvement (#52789); prefix_cache_retention_interval is now a CLI argument defaulting to 0 (#52216), with dense retention automatically restored for hybrid models using EAGLE/MTP (#55760, #55861).
• New defaults: FlashInfer all-reduce enabled by default for TP CUDA groups, opt out with VLLM_ALLREDUCE_USE_FLASHINFER=0 (#52998); prefix-cache NONE_HASH is deterministic by default so distributed KV cache users no longer need to pin PYTHONHASHSEED (#51875); new --max-num-queued-reqs / --max-num-queued-tokens admission-control flags (#49445).
• Breaking changes: ten deprecated model architectures removed (#53608); FlexOlmo, Olmo3 and Hunyuan V1/VL migrated to the Transformers modeling backend (#53615); PyAV video decoder backend removed (#54231); python -m vllm.entrypoints.openai.api_server deprecated in favor of vllm serve (#52131); VLLM_TEST_FORCE_FP8_MARLIN (#52182) and VLLM_ROCM_USE_AITER_FP4_ASM_GEMM (#53141) removed.

⚠️ Model Runner V1 Deprecation

Now that Model Runner V2 is used by default, we are considering Model Runner V1 deprecated and are targeting v0.32 for its removal. We do not intend to accept any more MRV1-specific improvements or optimizations.

Some features are not yet supported in MRV2 but we are planning for these gaps to be closed within the next 2-3 weeks. These include sequence parallelism, dual-batch overlap, elastic expert parallellism, custom logits processors and certain speculative decoding methods. For now, vLLM will still fall back to use MRV1 if any of these features are configured.

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/0.29.0/rocm723
XPU	uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.29.0/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:v0.29.0 (v0.29.0-cu130 also works)
CUDA 12.9	docker pull vllm/vllm-openai:v0.29.0-cu129
CUDA 13.0 + Ubuntu 24.04	docker pull vllm/vllm-openai:v0.29.0-ubuntu2404
CUDA 12.9 + Ubuntu 24.04	docker pull vllm/vllm-openai:v0.29.0-cu129-ubuntu2404
ROCm	docker pull vllm/vllm-openai-rocm:v0.29.0
CPU	docker pull vllm/vllm-openai-cpu:v0.29.0
XPU	docker pull vllm/vllm-openai-xpu:v0.29.0

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

... (truncated)

Commits

• 98dff2a [Bugfix][Core] Remove misleading Mamba prefix cache warning (#55863)
• 74c9692 [Bugfix][Core] Apply dense prefix cache default to hybrid models (#55861)
• 0c766af [Core] Default prefix_cache_retention_interval to dense for Mamba + EAGLE (#5...
• 15026e7 Revert "[CI] Remove deleted nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF1...
• d2906cc [Bugfix] Avoid sync in TRT-LLM ragged prefill
• 4cf572b [CI] Remove deleted nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 and it...
• 586f1d6 [Bugfix][Multimodal] Handle prefix-covered items in SHM worker cache (#54994)
• 82cbe53 [Bugfix][Core] Wait for the previous PP tensor sends before the next forward ...
• 316a4d5 [Bugfix] Log platform plugin detection failures (#52285)
• 33898f8 [Bugfix] Handle padded routes in CUTLASS MoE permutations (#54747)
• Additional commits viewable in compare view

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