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 functional P2P gating — one veto-only verdict for all P2P consumers (NCCL, custom allreduce, symm-mem)
- MooncakeConnector P/D: NVLink fallback transfer fails "Requested address not found" (fp8 MLA, v0.25.0)
- DeepSeek-V4 (deepseek_v4): fused topk_softplus_sqrt router rejects non-standard expert counts on CUDA (REAP 144-expert ckpts); torch fallback is XPU-gated
- deepseek_v4: decode runs unfused (breakable CUDA graph) — DeepseekV4ForCausalLM lacks @support_torch_compile; fullgraph capture blocked by inline deep_gemm/tilelang pybinds
- [KV Connector][NIXL] Support prefix caching with heterogeneous P/D logical block sizes
- Bump the minor-update group across 1 directory with 173 updates
- [Bug]: MooncakeConnector registers all workers as dp_rank=0 under multi-port external DP load balancing
- [Installation]: Worker processes hang in CPU deadloop after NCCL initialization when loading DeepSeek-V4-Flash-0731 with vLLM V1 engine on H100
- Fix missing q_scale fallback for FP8 KV cache
- [Frontend] Support allowed_tools in Rust chat requests
- Docs
- Python not yet supported