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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)

v0.10.1

Compare Source

Highlights

v0.10.1 release includes 727 commits, 245 committers (105 new contributors).

Model Support
  • New model families: GPT-OSS with comprehensive tool calling and streaming support (#​22327, #​22330, #​22332, #​22335, #​22339, #​22340, #​22342), Command-A-Vision (#​22660), mBART (#​22883), and SmolLM3 using Transformers backend (#​22665).
  • Vision-language models: Official Eagle multimodal support with Llama4 backend (#​20788), Step3 vision-language models (#​21998), Gemma3n multimodal (#​20495), MiniCPM-V 4.0 (#​22166), HyperCLOVAX-SEED-Vision-Instruct-3B (#​20931), Emu3 with Transformers backend (#​21319), Intern-S1 (#​21628), and Prithvi in online serving mode (#​21518).
  • Enhanced existing models: NemotronH support (#​22349), Ernie 4.5 Base 0.3B model name change (#​21735), GLM-4.5 series improvements (#​22215), Granite models with fused MoE configurations (#​21332) and quantized checkpoint loading (#​22925), Ultravox support for Llama 4 and Gemma 3 backends (#​17818), Mamba1 and Jamba model support in V1 (without CUDA graphs) (#​21249)
  • Advanced model capabilities: Qwen3 EPLB (#​20815) and dual-chunk attention support (#​21924), Qwen native Eagle3 target support (#​22333).
  • Architecture expansions: Encoder-only models without KV-cache enabling BERT-style architectures (#​21270), expanded tensor parallelism support in Transformers backend (#​22651), tensor parallelism for Deepseek_vl2 vision transformer (#​21494), and tensor/pipeline parallelism with Mamba2 kernel for PLaMo2 (#​19674).
  • V1 engine compatibility: Extended support for additional pooling models (#​21747) and Step3VisionEncoder distributed processing option (#​22697).
Engine Core
  • CUDA graph performance: Full CUDA graph support with separate attention routines, adding FA2 and FlashInfer compatibility (#​20059), plus 6% end-to-end throughput improvement from Cutlass MLA (#​22763).
  • Attention system advances: Multiple attention metadata builders per KV cache specification (#​21588), tree attention backend for v1 engine (experimental) (#​20401), FlexAttention encoder-only support (#​22273), upgraded FlashAttention 3 with attention sink support (#​22313), and multiple attention groups for KV sharing patterns (#​22672).
  • Speculative decoding optimizations: N-gram speculative decoding with single KMP token proposal algorithm (#​22437), explicit EAGLE3 interface for enhanced compatibility (#​22642).
  • Default behavior improvements: Pooling models now default to chunked prefill and prefix caching (#​20930), disabled chunked local attention by default for Llama4 for better performance (#​21761).
  • Extensibility and configuration: Model loader plugin system (#​21067), custom operations support for FusedMoe (#​22509), rate limiting with bucket algorithm for proxy server (#​22643), torch.compile support for bailing MoE (#​21664).
  • Performance optimizations: Improved startup time by disabling C++ compilation of symbolic shapes (#​20836), enhanced headless models for pooling in Transformers backend (#​21767).
Hardware & Performance
  • NVIDIA Blackwell (SM100) optimizations: CutlassMLA as default backend (#​21626), FlashInfer MoE per-tensor scale FP8 backend (#​21458), SM90 CUTLASS FP8 GEMM with kernel tuning and swap AB support (#​20396).
  • NVIDIA RTX 5090/RTX PRO 6000 (SM120) support: Block FP8 quantization (#​22131) and CUTLASS NVFP4 4-bit weights/activations support (#​21309).
  • AMD ROCm platform enhancements: Flash Attention backend for Qwen-VL models (#​22069), AITER HIP block quantization kernels (#​21242), reduced device-to-host transfers (#​22683), and optimized kernel performance for small batch sizes 1-4 (#​21350).
  • Attention and compute optimizations: FlashAttention 3 attention sinks performance boost (#​22478), Triton-based multi-dimensional RoPE replacing PyTorch implementation (#​22375), async tensor parallelism for scaled matrix multiplication (#​20155), optimized FlashInfer metadata building (#​21137).
  • Memory and throughput improvements: Mamba2 reduced device-to-device copy overhead (#​21075), fused Triton kernels for RMSNorm (#​20839, #​22184), improved multimodal hasher performance for repeated image prompts (#​22825), multithreaded async multimodal loading (#​22710).
  • Parallelization and MoE optimizations: Guided decoding throughput improvements (#​21862), balanced expert sharding for MoE models (#​21497), expanded fused kernel support for topk softmax (#​22211), fused MoE for nomic-embed-text-v2-moe (#​18321).
  • Hardware compatibility and kernels: ARM CPU build fixes for systems without BF16 support (#​21848), Machete memory-bound performance improvements (#​21556), FlashInfer TRT-LLM prefill attention kernel support (#​22095), optimized reshape_and_cache_flash CUDA kernel (#​22036), CPU transfer support in NixlConnector (#​18293).
  • Specialized CUDA kernels: GPT-OSS activation functions (#​22538), RLHF weight loading acceleration (#​21164).
Quantization
  • Advanced quantization techniques: MXFP4 and bias support for Marlin kernel (#​22428), NVFP4 GEMM FlashInfer backends (#​22346), compressed-tensors mixed-precision model loading (#​22468), FlashInfer MoE support for NVFP4 (#​21639).
  • Hardware-optimized quantization: Dynamic 4-bit quantization with Kleidiai kernels for CPU inference (#​17112), TensorRT-LLM FP4 quantization optimized for MoE low-latency inference (#​21331).
  • Expanded model quantization support: BitsAndBytes quantization for InternS1 (#​21953) and additional MoE models (#​21370, #​21548), Gemma3n quantization compatibility (#​21974), calibration-free RTN quantization for MoE models (#​20766), ModelOpt Qwen3 NVFP4 support (#​20101).
  • Performance and compatibility improvements: CUDA kernel optimization for Int8 per-token group quantization (#​21476), non-contiguous tensor support in FP8 quantization (#​21961), automatic detection of ModelOpt quantization formats (#​22073).
  • Breaking change: Removed AQLM quantization support (#​22943) - users should migrate to alternative quantization methods.
API & Frontend
  • OpenAI API compatibility: Unix domain socket support for local communication (#​18097), improved error response format matching upstream specification (#​22099), aligned tool_choice="required" behavior with OpenAI when tools list is empty (#​21052).
  • New API capabilities: Dedicated LLM.reward interface for reward models (#​21720), chunked processing for long inputs in embedding models (#​22280), AsyncLLM proper response handling for aborted requests (#​22283).
  • Configuration and environment: Multiple API keys support for enhanced authentication (#​18548), custom vLLM tuned configuration paths (#​22791), environment variable control for logging statistics (#​22905), multimodal cache size (#​22441), and DeepGEMM E8M0 scaling behavior (#​21968).
  • CLI and tooling improvements: V1 API support for run-batch command (#​21541), custom process naming for better monitoring (#​21445), improved help display showing available choices (#​21760), optional memory profiling skip for multimodal models (#​22950), enhanced logging of non-default arguments (#​21680).
  • Tool and parser support: HermesToolParser for models without special tokens (#​16890), multi-turn conversation benchmarking tool (#​20267).
  • Distributed serving enhancements: Enhanced hybrid distributed serving with multiple API servers in load balancing mode (#​21510), request_id support for external load balancers (#​21009).
  • User experience enhancements: Improved error messaging for multimodal items (#​22114), per-request pooling control via PoolingParams (#​20538).
Dependencies
  • FlashInfer updates: Updated to v0.2.8 for improved performance (#​21385), moved to optional dependency install with pip install vllm[flashinfer] for flexible installation (#​21959).
  • Mamba SSM restructuring: Updated to version 2.2.5 (#​21421), removed from core requirements to reduce installation complexity (#​22541).
  • Docker and deployment: Docker-aware precompiled wheel support for easier containerized deployment (#​21127, #​22106).
  • Python package updates: OpenAI Python dependency updated to latest version for API compatibility (#​22316).
  • Dependency optimizations: Removed xformers requirement for Mistral-format Pixtral and Mistral3 models (#​21154), deprecation warnings added for old DeepGEMM version (#​22194).
V0 Deprecation

Important: As part of the ongoing V0 engine cleanup, several breaking changes have been introduced:

  • CLI flag updates: Replaced --task with --runner and --convert options (#​21470), deprecated --disable-log-requests in favor of --enable-log-requests for clearer semantics (#​21739), renamed --expand-tools-even-if-tool-choice-none to --exclude-tools-when-tool-choice-none for consistency (#​20544).
  • API cleanup: Removed previously deprecated arguments and methods as part of ongoing V0 engine codebase cleanup (#​21907).

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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