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FlashDecoding++: Faster Large Language Model Inference on GPUs: Conclusion & Referencesby@textmodels

FlashDecoding++: Faster Large Language Model Inference on GPUs: Conclusion & References

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Due to the versatility of optimizations in FlashDecoding++, it can achieve up to 4.86× and 2.18× speedup on both NVIDIA and AMD GPUs compared to Hugging Face.
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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Authors:

(1) Ke Hong, Tsinghua University & Infinigence-AI;

(2) Guohao Dai, Shanghai Jiao Tong University & Infinigence-AI;

(3) Jiaming Xu, Shanghai Jiao Tong University & Infinigence-AI;

(4) Qiuli Mao, Tsinghua University & Infinigence-AI;

(5) Xiuhong Li, Peking University;

(6) Jun Liu, Shanghai Jiao Tong University & Infinigence-AI;

(7) Kangdi Chen, Infinigence-AI;

(8) Yuhan Dong, Tsinghua University;

(9) Yu Wang, Tsinghua University.

8 Conclusions

We propose FlashDecoding++, a fast Large Language Model inference engine in this paper. FlashDecoding++ accelerates mainstream LLMs with multiple hardware backend support. FlashDecoding++ proposes three novel designs: the asynchronized softmax with unified max value, the flat GEMM optimization with double buffering, and the heuristic dataflow with hardware resource adaption, achieving up to 4.86× and 2.18× speedup on NVIDIA and AMD GPUs compared with Hugging Face implementations. FlashDecoding++ also achieves an average of 1.37× speedup compared with state-of-the-art LLM inference engines, FlashDecoding, on various LLMs.

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