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
- Fix Kimi-K2.5 accuracy when Aiter MLA FP8 PS + CUDA graphs are used
- nano-nemotron-vl: get_mm_max_tokens_per_item for audio, video, image == seq_len
- [XPU] Enable group_size=-1/channel-wise for w4a16 and w4a8
- fused_moe_kernel opt
- [Feature]: Parity with CUDA: vLLM router should have ROCm CI
- [Bugfix] Correct mistake in chained comparison in static assert logic
- [Refactor] Merge duplicate checks and error handling in Executor
- [OPT] Optimize the fused moe triton kernel routing expert accumulation
- [Quantization] Convert NVFP4 weights to FP8 on Hopper for faster inference
- [Core] Preempt requests with fewer num_computed_tokens to reduce wasted computation
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