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
- Enable FlashInfer + FP8 KV cache for text-only requests in Gemma 4 multimodal models
- [Bug] OOM during profile_run with GLM-5.2 PD disaggregation + FlashInfer CUTLASS MoE on H100
- [Bug] v0.24.0: DeepGEMM "Unknown recipe" assertion in FP8 kernel warmup on Blackwell (sm_120) — regression vs 0.23.0
- [Bug]: cached_tokens always equals prompt_tokens in disaggregated prefill (P/D) on the decode node
- Allow GLM-4.7 required tool parsing when strict mode is disabled
- [RFC]: Streaming Derender for Disaggregated Serving
- [Bug]: Image URL errors return HTTP 500 instead of 422 for unprocessable content
- [Tool Parser] poolside_v1: migrate to the declarative parser engine
- [Usage]: vLLM 0.24.0 crashes on startup with Qwen3.6-27B-FP8 on Blackwell SM120 — DeepGemm warmup ignores auto-disable
- [CI] Add GSM8K accuracy configs for Blackwell NVFP4/FP8 models
- Docs
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