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]: Experimental Recirculation for causal decoders
- [Bug]: DeepSeek v4 Pro Base - The default MoE backend selection produces incorrect output and inflated logprobs (FlashInfer TRTLLM vs deep_gemm)
- fix(quant): bypass fc quantization for compressed-tensors MTP checkpo…
- [Rust Frontend] /derender: reasoning and tool-call parsing (phase 2/3)
- [Rust Frontend] /derender: streaming derender + two-process e2e test (phase 3/3)
- [Performance]: DSD K=0 draft-state sync forward costs 4-11% in a K≡0 workload; resume impact differs sharply between the two tested drafter architectures (MTP vs EAGLE3)
- [RFC]: First-Class, Orchestrator-Agnostic KV Hint Envelope for Agentic Workloads
- [Bug]: INSTALL_KV_CONNECTORS=true in vLLM 0.27.1 Docker image installs LMCache wheel incompatible with shipped PyTorch, causing lmcache.c_ops to fall back to torch baseline
- [Bugfix][DSv4] SM12x FlashInfer sparse MLA kernel block size 64
- [Core][Spec Decode] Opt-in skip of the K=0 draft sync forward (MTP + DFlash, default off)
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