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
- [Feature]: SubSpec — Lossless Training-Free Speculative Decoding for CPU-Offloaded LLMs via Quantized Substitute Draft
- [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
- fix(minimax_m2): avoid KeyError on split q/k/v NVFP4 weight scales
- [Tracing] Extend OpenTelemetry instrumentation to remaining HTTP route handlers
- [Feature]: Support sparse in-place weight updates in weight transfer API
- [torch.compile] E2E correctness testing for fusions
- [ROCm][Bugfix] add swiglustep triton fallback
- Use physical device id for get_device_uuid
- Docs
- Python not yet supported