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
- [Compile] Add autotune_batch_hint config for dynamic shapes
- [Bug]: The content of response from Kimi-K2.5 is empty.
- [Model] Add support for Solar Open
- [Kernel][Helion][1/N] Add Helion kernel for scaled_mm
- [RFC]: Enable reproducible benchmarking in benchmark_serving_multi_turn with API-usage token counts
- [RFC]: Improve Dynamo compile times 5-10x via unsafe assumptions
- [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
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