DeepSpeed-FastGen #4604
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DeepSpeed-FastGen #4604
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Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com> Co-authored-by: Ammar Ahmad Awan <ammar.awan@microsoft.com> Co-authored-by: Connor Holmes <connorholmes@microsoft.com> Co-authored-by: Masahiro Tanaka <mtanaka@microsoft.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
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Co-authored-by: Jeff Rasley <jerasley@microsoft.com> Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com> Co-authored-by: Ammar Ahmad Awan <ammar.awan@microsoft.com> Co-authored-by: Masahiro Tanaka <mtanaka@microsoft.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
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DeepSpeed-FastGen is built to leverage continuous batching and non-contiguous KV caches to enable increased occupancy and higher responsivity for serving LLMs in the data center, similar to existing frameworks such as TRT-LLM, TGI, and vLLM. In order to achieve a new level of performance, DeepSpeed-FastGen introduces SplitFuse which leverages dynamic prompt and generation decomposition and unification to further improve continuous batching and system throughput.
Corresponding blog: #4607