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fix(deps): update dependency vllm to ^0.10.0 #257

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@dreadnode-renovate-bot dreadnode-renovate-bot bot commented Aug 21, 2025 •
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This PR contains the following updates:

Package Change Age Confidence
vllm ^0.5.0 -> ^0.10.0 age confidence

Release Notes

vllm-project/vllm (vllm)

v0.10.2

Compare Source

Highlights

This release contains 740 commits from 266 contributors (97 new)!

Breaking Changes: This release includes PyTorch 2.8.0 upgrade, V0 deprecations, and API changes - please review the changelog carefully.

aarch64 support: This release features native support for aarch64 allowing usage of vLLM on GB200 platform. The docker image vllm/vllm-openai should already be multiplatform. To install the wheels, you can download the wheels from this release artifact or install via

uv pip install vllm==0.10.2 --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto
Model Support
Engine Core
  • V1 engine maturation: Extended V1 support to compute capability < 8.0 (#​23614, #​24022), added cross-attention KV cache for encoder-decoder models (#​23664), request-level logits processor integration (#​23656), and KV events from connectors (#​19737).
  • Backend expansion: Terratorch backend integration (#​23513) enabling non-language model tasks like semantic segmentation and geospatial applications with --model-impl terratorch support.
  • Hybrid and Mamba model improvements: Enabled full CUDA graphs by default for hybrid models (#​22594), disabled prefix caching for hybrid/Mamba models (#​23716), added FP32 SSM kernel support (#​23506), full CUDA graph support for Mamba1 (#​23035), and V1 as default for Mamba models (#​23650).
  • Performance core improvements: --safetensors-load-strategy for NFS based file loading acceleration (#​24469), critical CUDA graph capture throughput fix (#​24128), scheduler optimization for single completions (#​21917), multi-threaded model weight loading (#​23928), and tensor core usage enforcement for FlashInfer decode (#​23214).
  • Multimodal enhancements: Multimodal cache tracking with mm_hash (#​22711), UUID-based multimodal identifiers (#​23394), improved V1 video embedding estimation (#​24312), and simplified multimodal UUID handling (#​24271).
  • Sampling and structured outputs: Support for all prompt logprobs (#​23868), final logprobs (#​22387), grammar bitmask optimization (#​23361), and user-configurable KV cache memory size (#​21489).
  • Distributed: Support Decode Context Parallel (DCP) for MLA (#​23734)
Hardware & Performance
  • NVIDIA Blackwell/SM100 generation: FP8 MLA support with CUTLASS backend (#​23289), DeepGEMM Linear with 1.5% E2E throughput improvement (#​23351), Hopper DeepGEMM E8M0 for DeepSeekV3.1 (#​23666), SM100 FlashInfer CUTLASS MoE FP8 backend (#​22357), MXFP4 fused CUTLASS MoE (#​23696), default MXFP4 MoE on Blackwell (#​23008), and GPT-OSS DP/EP support with 52,003 tokens/s throughput (#​23608).
  • Breaking change: FlashMLA disabled on Blackwell GPUs due to compatibility issues (#​24521).
  • Kernel and attention optimizations: FlashAttention MLA with CUDA graph support (#​14258, #​23958), V1 cross-attention support (#​23297), FP8 support for FlashMLA (#​22668), fused grouped TopK for MoE (#​23274), Flash Linear Attention kernels (#​24518), and W4A8 support on Hopper (#​23198).
  • Performance improvements: 13.7x speedup for token conversion (#​20413), TTIT/TTFT improvements for disaggregated serving (#​22760), symmetric memory all-reduce by default (#​24111), FlashInfer warmup during startup (#​23439), V1 model execution overlap (#​23569), and various Triton configuration tuning (#​23748, #​23939).
  • Platform expansion: Apple Silicon bfloat16 support for M2+ (#​24129), IBM Z V1 engine support (#​22725), Intel XPU torch.compile (#​22609), XPU MoE data parallelism (#​22887), XPU Triton attention (#​24149), XPU FP8 quantization (#​23148), and ROCm pipeline parallelism with Ray (#​24275).
  • Model-specific optimizations: Hardware-tuned MoE configurations for Qwen3-Next on B200/H200/H100 (#​24698, #​24688, #​24699, #​24695), GLM-4.5-Air-FP8 B200 configs (#​23695), Kimi K2 optimization (#​24597), and QWEN3 Coder/Thinking configs (#​24266, #​24330).
Quantization
  • New quantization capabilities: Per-layer quantization routing (#​23556), GGUF quantization with layer skipping (#​23188), NFP4+FP8 MoE support (#​22674), W4A8 channel scales (#​23570), and AMD CDNA2/CDNA3 FP4 support (#​22527).
  • Advanced quantization infrastructure: Compressed tensors transforms for linear operations (#​22486) enabling techniques like SpinQuantR1R2R4 and QuIP quantization methods.
  • FlashInfer quantization integration: FP8 KV cache for TRTLLM prefill attention (#​24197), FP8-qkv attention kernels (#​23647), and FP8 per-tensor GEMMs (#​22895).
  • Platform-specific quantization: ROCm TorchAO quantization enablement (#​24400) and TorchAO module swap configuration (#​21982).
  • Performance optimizations: MXFP4 MoE loading cache optimization (#​24154) and compressed tensors version updates (#​23202).
  • Breaking change: Removed original Marlin quantization format (#​23204).
API & Frontend
  • OpenAI API enhancements: Gemma3n audio transcription/translation endpoints (#​23735), transcription response usage statistics (#​23576), and return_token_ids parameter (#​22587).
  • Response API improvements: Streaming support for non-harmony responses (#​23741), non-streaming logprobs (#​23319), MCP tool background mode (#​23494), MCP streaming+background support (#​23927), and tool output token reporting (#​24285).
  • Frontend optimizations: Error stack traces with --log-error-stack (#​22960), collective RPC endpoint (#​23075), beam search concurrency optimization (#​23599), unnecessary detokenization skipping (#​24236), and custom media UUIDs (#​23449).
  • Configuration enhancements: Formalized --mm-encoder-tp-mode flag (#​23190), VLLM_DISABLE_PAD_FOR_CUDAGRAPH environment variable (#​23595), EPLB configuration parameter (#​20562), embedding endpoint chat request support (#​23931), and LM Format Enforcer V1 integration (#​22564).
Dependencies
  • Major updates: PyTorch 2.8.0 upgrade (#​20358) - breaking change requiring environment updates, FlashInfer v0.3.0 upgrade (#​24086), and FlashInfer 0.2.14.post1 maintenance update (#​23537).
  • Supporting updates: XGrammar 0.1.23 (#​22988), TPU core dump fix with tpu_info 0.4.0 (#​23135), and compressed tensors version bump (#​23202).
  • Deployment improvements: FlashInfer cubin directory environment variable (#​22675) for offline environments and pre-cached CUDA binaries.
V0 Deprecation
  • Backend removals: V0 Neuron backend deprecation (#​21159), V0 pooling model support removal (#​23434), V0 FlashInfer attention backend removal (#​22776), and V0 test cleanup (#​23418, #​23862).
  • API breaking changes: prompt_token_ids fallback removal from LLM.generate and LLM.embed (#​18800), LoRA extra vocab size deprecation warning (#​23635), LoRA bias parameter deprecation (#​24339), and metrics naming change from TPOT to ITL (#​24110).
Breaking Changes
  1. PyTorch 2.8.0 upgrade - Environment dependency change requiring updated CUDA versions
  2. FlashMLA Blackwell restriction - FlashMLA disabled on Blackwell GPUs due to compatibility issues
  3. V0 feature removals - Neuron backend, pooling models, FlashInfer attention backend
  4. Quantizations - Removed quantized Mixtral hack implementation, and original Marlin format.
  5. Metrics renaming - TPOT deprecated in favor of ITL

What's Changed

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Enabled.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR has been generated by Renovate Bot.

@dreadnode-renovate-bot
fix(deps): update dependency vllm to ^0.10.0
b262dd5 
| datasource | package | from  | to     |
| ---------- | ------- | ----- | ------ |
| pypi       | vllm    | 0.5.5 | 0.10.1 |
@dreadnode-renovate-bot dreadnode-renovate-bot bot requested a review from a team as a code owner August 21, 2025 20:04
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⚠️ Artifact update problem

Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.

♻ Renovate will retry this branch, including artifacts, only when one of the following happens:

  • any of the package files in this branch needs updating, or
  • the branch becomes conflicted, or
  • you click the rebase/retry checkbox if found above, or
  • you rename this PR's title to start with "rebase!" to trigger it manually

The artifact failure details are included below:

File name: poetry.lock
Updating dependencies
Resolving dependencies...


The current project's supported Python range (>=3.10,<3.14) is not compatible with some of the required packages Python requirement:
  - vllm requires Python <3.13,>=3.9, so it will not be installable for Python >=3.13,<3.14
  - vllm requires Python <3.13,>=3.9, so it will not be installable for Python >=3.13,<3.14
  - vllm requires Python <3.13,>=3.9, so it will not be installable for Python >=3.13,<3.14

Because no versions of vllm match >0.10.0,<0.10.1 || >0.10.1,<0.10.1.1 || >0.10.1.1,<0.11.0
 and vllm (0.10.0) requires Python <3.13,>=3.9, vllm is forbidden.
And because vllm (0.10.1) requires Python <3.13,>=3.9, vllm is forbidden.
So, because vllm (0.10.1.1) requires Python <3.13,>=3.9
 and rigging depends on vllm (^0.10.0), version solving failed.

  * Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties

    For vllm, a possible solution would be to set the `python` property to ">=3.10,<3.13"
For vllm, a possible solution would be to set the `python` property to ">=3.10,<3.13"
For vllm, a possible solution would be to set the `python` property to ">=3.10,<3.13"

    https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
    https://python-poetry.org/docs/dependency-specification/#using-environment-markers

@dreadnode-renovate-bot dreadnode-renovate-bot bot added type/digest Dependency digest updates area/python Changes to Python package configuration and dependencies labels Aug 21, 2025
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