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
- [Bug]: DeepSeek-V4-Flash-0731 + DSpark fails on RTX PRO 6000 (SM120) with FlashInfer sparse MLA decode kernel routing
- [Bugfix][Model] Kimi-K3 NVIDIA: delegate regular FusedMoE padding to the selected quantization backend
- [Feature]: Kimi K3 Performance Optimization
- fix(config): serialize remote lazy config mappings
- [Docs] Fix stale FusedMoE references in custom quantization guide
- [Bug]: LoRA linear path (lora_shrink/lora_expand) nondeterministic in serving; only an fp32-eager interior is stable — full elimination matrix
- [Bug] Kimi-K3: tool-call arguments don't stream — entire payload emitted in one SSE delta after minutes of silence
- [TPU] Exempt TPU from the V2 model runner PCP and Triton gates
- No capability flag declares which KV-cache kinds may be peeked past a candidate boundary — each margin implementation excludes Mamba by hand
- [Helion] Require Helion for CUDA builds
- Docs
- Python not yet supported