MacOS LLM Controller Performance Optimization Guide
The following optimization strategies can be implemented to address the problem of high system resource usage:
- Hardware Adjustment::
- Allocating more memory for Ollama: Execute the
export OLLAMA_MAX_MEMORY=10GB(adjusted to machine configuration) - Enable GPU acceleration: run
ollama run llama3.2:3b-instruct-fp16 --gpu
- Allocating more memory for Ollama: Execute the
- Software Configuration::
- Limit concurrent requests: in
backend/config.pyset up inMAX_CONCURRENT_REQUESTS=1 - Using quantitative models: replace
llama3.2:3b-instruct-q4Version Reduces Compute Load
- Limit concurrent requests: in
- System-level optimization::
- Shut down extraneous processes: terminate CPU/memory-hogging applications via activity monitor
- Setting task priority: terminal execution
renice -n -20 -p [ollama_pid]
For developers, it is recommended to 1) analyzedocker statsMonitor container resources 2) Use Instruments tools for performance analysis 3) Consider upgrading to an M-series chip Mac for best performance.
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