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
- [Perf] Add H200 BF16 fused MoE configs for Gemma4 (E=128,N=704)
- [Bugfix][CI] Retry cached HF tokenizer load after transport failures
- [RFC]: MTP Routing for Qwen3.5 Series Multi-LoRA Deployments
- [Bug]: Inference-time probabilistic error: pre-allocated buffer size mismatch in indexer
- [CPU][Zen] Route Int8 MoE inference through zentorch on AMD
- [Kernel][Perf] Add moe_sum_kernel specializations for topk=5-8
- [Performance]: After enabling MTP on the Qwen3.5-27B model, the number of hit blocks for the prefix cache is one less compared to the scenario with MTP disabled. This is the current implementation. Can we optimize this behavior?
- [Bug]: EngineDeadError: RPC call to execute model timed out on CPU when running google/gemma-4-26B-A4B-it with large concurrent decode batch`
- [KV Offload] Reshape the transfer data model: per group specs and offloaded side alignment offset
- [Feature]: [CPU Backend] Support macOS x86 for CPU backend
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- Python not yet supported