Fluency Optimization Practice Solution
The following performance optimization strategies can be used when multiple large models are invoked at the same time:
- batch load: Enable "Sequential Loading" mode in settings (experimental feature) to show model responses one by one.
- Model Preferences: Avoid selecting multiple large models above 70B parameters at the same time and mix and match small and medium models
- hardware acceleration: Enable GPU acceleration in Chrome (chrome://flags/#enable-gpu-rasterization)
- network optimization: Configure HTTP/2 protocol at deployment time to reduce API request header overhead
Monitoring method: Observe the Waterfall chart on the Network tab in the browser developer tools to identify the slowest responding model API endpoints. It is recommended that enterprise users consider localized deployment of the Model Gateway.
This answer comes from the articleOpen-Fiesta: an open source tool for chatting with multiple AI macromodels at onceThe
May not be reproduced without permission:AI productivity tools " How to prevent interface lag caused by multiple model responses?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil