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
- [CI/Build] Remove stale xfail marks from min_tokens e2e tests
- [Bugfix] Allow untyped tool schemas to coerce JSON values
- [Feature]: Support NVIDIA Nemotron 3.5 ASR Streaming (nemotron-3.5-asr-streaming-0.6b)
- [Perf] Maintain persistent penalty statistics instead of per-step CPU rebuild in V1 sampler
- [Bug]: Incoherent output depending on tensor-parallel-size during inference on Leanstral (Mistral Small 4 family)
- [Bug]: CPU DP affinity regression
- [V1][Spec Decode] Overlap bonus sampling with target verification via maybe_execute_in_parallel
- [ROCm][Perf] Enable fused indexer-Q RoPE+quant kernel for DeepSeek/GLM sparse attention
- Respect explicit VLLM_TARGET_DEVICE=tpu on macOS
- [Bugfix][Build] Respect explicit VLLM_TARGET_DEVICE on macOS
- Docs
- Python not yet supported