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ByteDance details attention, MoE and speculative decoding work for faster Hopper inference

2 articles1 sourcesince Nov 4Last article 1h ago ·

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ByteDance engineer Ho-Ren (Jack) Chuang shares the attention, MoE, and speculative decoding work behind faster open-model inference on Hopper GPUs using SGLang, according to LMSYS Org's X post.

The post names the three areas but gives no benchmark figures or speedup numbers, so the size of the improvement is not reported.

SGLang is the open-source serving framework that LMSYS Org develops.

Written by AI from the articles below · updated Nov 4, 5:54 PM ET

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Latest developmentNov 4, 5:51 PM ET
ByteDance explains faster open-model inference on Hopper with SGLang

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The articles in this story. Times are ET.

Nov 4
  1. LMSYS OrgOfficial
    ByteDance explains faster open-model inference on Hopper with SGLang

    AIByteDance engineer Ho-Ren (Jack) Chuang describes the attention, MoE, and speculative decoding work behind faster open-model inference on Hopper GPUs using SGLang. The post shares the techniques without giving benchmark figures or specific speedups.

    Image from @lmsysorg's post
  2. LMSYS OrgOfficial
    ByteDance explains faster open-model inference on Hopper with SGLang

    AIByteDance engineer Ho-Ren (Jack) Chuang shares the attention, MoE, and speculative decoding work behind faster open-model inference on Hopper GPUs using SGLang. The post covers these three areas, which the source does not quantify with speedup figures.

    Image from @lmsysorg's post

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