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
- [Bug]: DeepSeekV3.1 with fp8 kvcache in v0.15.0 produces garbled output
- [RFC]: Enable reproducible benchmarking in benchmark_serving_multi_turn with API-usage token counts
- [RFC]: Improve Dynamo compile times 5-10x via unsafe assumptions
- [Bugfix] Handle case when kimi ends reasoning with a tool call
- [Feature] Support manually enabling the cumem allocator
- [Benchmark] Enable reproducible benchmarking with API-usage token counts
- [DRAFT] Enable torch.compile on Gemma3n_mm Multimodal Embedding Layer
- [Feature]: improve sleep mode to not break torch.cuda memory counters
- [Bug]: Failed to run distributed inference with Gloo backend on aarch64
- [Doc]: When running benchmarks, the prefix cache hit rate is abnormally high (not consistent with the production environment).
- Docs
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