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
- [RFC]: Unify the Weight Loading Lifecycle Across Initial Load and Reload
- [Feature]: FlashInfer-CUTLASS NVFP4 MoE lacks SWIGLUSTEP activation (blocks Step-3.7-Flash-NVFP4 at TP=8)
- [Bugfix][Model] Fix MiniMax-M3 NVFP4 inference correctness
- [Bug] DeepSeek-V4 tool-call parser leaks raw DSML into content when the model omits the `<｜DSML｜tool_calls>` START token (long context)
- [Spec Decode] Fix speculators-format DSpark loading; mirror repetition penalty on DSpark draft logits
- [Benchmark] KV Cache Offload Benchmark — Block Copy Performance
- [ROCm] Support MiniMax-M3 NVFP4 SwiGLU-OAI
- Fix special-token leakage into content when tools/tool_choice is omitted on a later agent-loop turn
- [Docs] Add Llama-3.1-8B-Instruct to batch invariance tested models
- [Bug][XPU]: gpt-oss-120b (MXFP4 MoE, TP=2) corrupt under piecewise XPU graph capture on Data Center GPU Max 1100 (PVC); compile-mode correct
- Docs
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