LLM RAM Calculator

Running Large Language Models locally requires significant memory resources. Here's what you need to know:

Key Factors Affecting Memory Usage

• Model Size: The number of parameters directly impacts memory requirements. Larger models (more parameters) need more RAM.
• Precision: Models can run at different precision levels:
  • Full precision (32-bit floats): Highest quality, highest memory usage
  • Half precision (16-bit floats): Good quality, ~50% memory reduction
  • 8-bit quantization: Reasonable quality, ~75% memory reduction
  • 4-bit quantization: Lower quality, ~87.5% memory reduction
• Context Length: Longer context windows require proportionally more memory.

Recommended Hardware

• Entry-level (8-16GB RAM): Suitable for 1-3B parameter models with quantization
• Mid-range (32GB RAM): Can handle 7B parameter models at higher precision or 13B with quantization
• High-end (64GB+ RAM): Required for 70B parameter models even with quantization
• Server-grade (128GB+ RAM): Needed for the largest open-source models or for running multiple models simultaneously

Balancing Memory Usage and Performance

• Use quantization to reduce memory requirements, but be aware it may slightly impact output quality
• Choose models with fewer parameters for faster inference on limited hardware
• Consider using a GPU with VRAM instead of system RAM for better performance
• Adjust context length settings to optimize for your specific use case
• Try specialized formats like GGUF (formerly GGML) which are optimized for efficient inference