AI memory management

Document and implement proper memory management strategies for AI model inference to prevent out-of-memory errors and optimize performance. When developing AI applications or documentation:

1. Specify hardware memory requirements
   • Document minimum and recommended memory configurations
   • For GPU-accelerated models, specify VRAM requirements
   • Example: “Set UMA for iGPU in BIOS (at least >1GB, recommend >8GB for Llama3:8b q4_0 model size is 4.7GB)”
2. Configure context window appropriately
   • Set context window size based on available memory and use case requirements
   • Example: OLLAMA_CONTEXT_LENGTH=8192 ollama serve
3. Implement memory optimization techniques
   • Use environment variables to control memory usage:

     # Reserve additional GPU memory buffer
     OLLAMA_GPU_OVERHEAD=536870912 ollama serve
          
     # Enable more efficient memory usage
     OLLAMA_FLASH_ATTENTION=1 ollama serve
          
     # Allow GPU to use CPU memory (Linux only)
     GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 ollama serve
          
     # Control parallel processing
     OLLAMA_NUM_PARALLEL=1 ollama serve
     

4. Document fallback strategies
   • Provide clear instructions for reducing memory requirements when needed
   • Include troubleshooting steps for memory-related errors

By explicitly documenting memory requirements and optimization strategies, you ensure reliable operation of AI models across different environments and hardware configurations.