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][V1][Hybrid] IndexError in get_temporal_copy_spec during DFlash speculative decoding + prefix caching
- [Feature]: NIXL P/D Disaggregation: GDN support (Qwen3.5)
- Bug report: vLLM gpt-oss-120b returns content=null, reasoning=null with completion_tokens > 0
- fixed kimi k2.5 dflash
- [XPU][WIP][CT] support mxfp8 moe model.
- [Bug]: get_expert_mapping is inconsistent with load_weights for EPLB in DeepseekV2 and AXK1
- [Performance]: Inspired by nano-vllm, as vLLM-Omni is also complex, I tried building a nano-vLLM-Omni (~1k LOC)
- [Bug]: Wrongful detection of WSL
- [Bug]: DeepGemmExperts FP8 MoE drops swiglu_limit (DeepSeek-V4-Flash-Base produces glitch tokens)
- [Feature]: Opt-in: stop caching KV blocks after thinking start tokens
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