Hardware Resource Optimization Guide
A solution for low-configuration environments:
- Model Selection Strategy: Set MODEL_SIZE=medium in the .env file to use a streamlined version of the language model (40% smaller than the original model)
- Batch Configuration: Adjust BATCH_SIZE=2 in Docker-compose.yml to reduce peak memory usage
- Disk Cache Utilization: Add the PERSIST_CACHE=true parameter after the first run to avoid duplicate downloads of the model
- Port Optimization: Limit the number of concurrent agents when running a single task MAX_AGENTS=3
Measured data: 4GB memory device after optimization, document processing speed can reach the standard configuration of 65%. It is recommended to shut down other processes that occupy the GPU, and prioritize to ensure that the embedded model is running.
This answer comes from the articleMAESTRO: In-depth research assistant with local knowledge base and multi-agent collaborationThe
May not be reproduced without permission:AI productivity tools " How to optimize MAESTRO's performance on limited hardware?
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