Optimization solutions
The following measures are recommended for performance issues with local deployments:
- Basic Configuration::
- Minimum 16GB RAM, 32GB recommended for complex tasks
- Accelerated model inference with NVIDIA RTX 3060 and above GPUs
- SSD storage ensures data read and write speeds
- Software Optimization::
- exist
config.yamlEnable model quantification in:quantization: 8bit - Limit the number of concurrent intelligences (default 5, adjustable to 3)
- exist
- alternative::
- Hybrid deployments: passing compute-intensive tasks through
api_keyConfiguring Routing to Cloud Models - Isolating Resource-Sensitive Tasks with Docker Containers
- Hybrid deployments: passing compute-intensive tasks through
Tests show that these optimizations can increase task execution speed by 2-3 times
This answer comes from the articleEigent: an open source desktop application for automated multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " How to resolve hardware performance bottlenecks when deploying Eigent locally?
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