Xiaomi shares long-context MiMo inference lessons using SGLang
Overview
Xiaomi engineers Chen Zhang and Yingchun Lai describe how the company handles KV caches, disaggregated serving, and scheduling for long-context MiMo workloads with SGLang, according to a post from the LMSYS Org account on X. The post presents these methods as lessons from Xiaomi's production inference setup.
It does not list benchmark figures.
Written by AI from the articles below · updated Nov 4, 5:54 PM ET
Check the sources:
Article timeline
The articles in this story. Times are ET.
LMSYS Org@lmsysorgOfficialXiaomi shares lessons on long-context MiMo inference with SGLangAIXiaomi engineers Chen Zhang and Yingchun Lai describe how the company handles KV caches, disaggregated serving, and scheduling for long-context MiMo workloads using SGLang. The post presents these approaches as lessons from Xiaomi's MiMo deployments.

LMSYS Org@lmsysorgOfficialXiaomi explains long-context inference serving for MiMo with SGLangAIXiaomi engineers Chen Zhang and Yingchun Lai describe how the company handles KV caches, disaggregated serving, and scheduling for long-context MiMo workloads using SGLang. The post shares lessons from Xiaomi's production inference setup without listing benchmark figures.

Heat Trend
Not enough continuous observations to show a trend yet.