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
- [Feature]: Universal Speculative Decoding for Heterogeneous Vocabularies (TLI / Token-Level Intersection)
- [RFC]: Support ViT Full CUDA Graph (Tracker)
- [Bug]: Based on Qwen3.5-35B-A3B, why does enabling MTP speculative decoding actually reduce the prefix cache hit rate?
- [code clean] remove useless contextlib.suppress(Exception)
- [Performance]: Prefix cache hit lower on vLLM than on other inference stacks
- [Feature]: Add apply_with_spec_decode() method to LogitBiasLogitsProcessor for speculative decoding support
- [Bug]: M2.5 tool call result is badcase, deploy 1p1d with nixl connector, P and D use DP8-EP-TP1
- [Bug]: CUDA Illegal Instruction during CUDA Graph capture with Nemotron-3-Nano NVFP4 on sm_121
- [Bug]: Based on vllm 0.18.0 version, when the number of tensor parallelizations is greater than 1, an error message will be reported: [AMP ERROR] [CudaFrontend. cpp: 94] [failed to call cuCtxGetDevice (&device), error code: CUDA-ERROR-INVALIDFHIR TEXT
- Hybrid KV offload: MultiConnector + planner for mamba+attention models
- Docs
- Python not yet supported