vllm
https://github.com/vllm-project/vllm
Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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- Issues
- [Model] Support per-layer sliding window attention for Qwen3
- [Core] Use standalone autograd_cache_key for compilation dedup optimization
- [Bugfix][V1] Retain encoder cache entries for multimodal models under async scheduling
- [Feature]: SubSpec — Lossless Training-Free Speculative Decoding for CPU-Offloaded LLMs via Quantized Substitute Draft
- [Installation]: Vllm doesnt have any failed installation handling.
- [Bug]: vllm 0.19.0, gemma4, The format of the tool call returned by vllm is incorrect.
- [Bug] Regression: GPTQ models fail to load on Intel XPU in v0.19.0 (missing XPU branches in gptq.py)
- [torch.compile] config hashing refactor follow-ups
- [Bugfix] Fix Triton stream capture error on A100 in GDN attention with MTP speculative decoding
- [Bugfix] Add deepseek_v32 to Quark dynamic MXFP4 model type check
- Docs
- Python not yet supported