LLM Memory Calculator

Different hardware architectures handle memory differently. Apple Silicon uses unified memory, while dedicated GPUs have their own memory.

The parameter count determines model complexity and capabilities. Larger models generally offer better performance but require more computational resources.

Lower precision reduces memory needs and increases inference speed, but may impact model performance. 4-bit and 8-bit quantization are common methods to reduce resource requirements.

Training requires additional memory for gradients and optimizer states. Inference uses the least memory, while training (especially from scratch) needs significantly more resources.