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
- [Usage]: ROCm gfx950: MXFP4 activation quant not fused into GEMM; Quark INT4 W4A16 uses a very slow Triton kernel
- [Bugfix][Frontend] Honor allowed_tools tool_choice in the Responses API
- [Bug]: Exllama GPTQ and RDNA3 W4A16 kernels silently produce wrong output when `group_size % 32 != 0` (reproduced on CUDA sm_120 and ROCm gfx1100)
- [Bugfix][Structured Output] Flag combinators with sibling constraints…
- [Bug]: FP8 KV cache startup can OOM because CUDA graph memory is omitted from the cache budget
- [ROCm] [Feat] Enable HiSparse on ROCm
- [Bug]: AOT-compiled artifact loading is decided per rank, so a partial load leaves ranks on different startup paths
- [Feature][Observability] Add production-ready Grafana dashboards
- [Bug]: Olmo3 tool parser corrupts string arguments with newlines and drops calls split across lines
- [BugFix] Keep newlines in Olmo3 tool-call arguments
- Docs
- Python not yet supported