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]: 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
- [Bug]: Gemma-4 fails when forcing FLASHINFER attention backend on Blackwell SM120 (head_size not supported)
- [Bugfix] fix incorrect apply_interleaved_rope in mrope under torch.compile
- [ROCm][Perf] Support N=5 in wvSplitK skinny GEMM kernels for speculative decoding
- [Bug]: Max token length incorrect when /nothink tag on Qwen3.5-4B
- [Bug]: The KV cache size log is wrong for Qwen3.5
- [Bug]: RoutedExpertsCapturer host buffer undersized for hybrid models with multiple KV cache groups
- [Bug]: ValueError: Gemma4ForConditionalGeneration does not support LoRA yet.
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