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
- [torch.compile] Add hierarchical module trace dump for FX graphs
- [Attention][TurboQuant] Optimize k8v4 decode attention with GQA head grouping
- [Attention] Enable TRITON_MLA MTP full CUDA graphs for Kimi on Blackwell
- [CI/Build] Make opencv-python-headless an optional dependency
- KimiK25ForConditionalGeneration failed to be inspected — SIGSEGV in registry subprocess during process exit (GB200)
- [Bug]: KeyError on model.layers.N.self_attn.attn during initialize_attn_backend with pipeline_parallel_size=4 (V1 engine + Ray)
- [Bug]: Kimi 2.6 on 8x A100 SMX4 leads to NVLink Crash Coredump
- [Bug]: CUBLAS_STATUS_EXECUTION_FAILED during CUDA graph compilation of BF16 vision encoder on NVIDIA Jetson AGX Thor (vLLM 0.19.0 regression)
- [Feature]: Support NixlConnector with Pipeline Parallelism for disaggregated serving
- [RFC] Support Intel Quantization Toolkit AutoRound on Intel Platforms
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