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
- [Bugfix] Preserve truncated Kimi K3 reasoning
- VLLM_ATTENTION_BACKEND env var is silently ignored — attention_backend= LLM()/EngineArgs kwarg is the only mechanism that works
- [Kernel][Model] Optimize FA4 mm_prefix range lookup
- [Test] Add per-parser tool-call format conformance tests
- [Core] Add TP-invariant tree kernels across TP sizes
- [ROCm] Implement is_integrated_gpu() for APU unified-memory accounting
- [Perf] Skip detokenization in offline beam search
- DCP: consume owner-sharded Top-K candidates through symmetric memory
- [Bug]: fp8/bfloat16 KV cache does a full NVML init+shutdown per attention layer per step
- [RFC]: Support Speculative Decoding with Decode Context Parallelism
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