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: ```bash # 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.