5 Things to Know About RAM for Running LLMs

VRAM matters more than system RAM for GPU inference When running models on a GPU, VRAM is the main bottleneck. A 7B model in 4-bit quantization needs about 6-8GB of VRAM, and full precision can use 14GB or more.

32GB system RAM is a sweet spot for CPU inference For CPU-based inference using tools like llama.cpp or Ollama, 32GB of system RAM is plenty. A 7B model in 4-bit uses only 4-6GB, leaving room for context and multitasking.

System RAM won’t help GPU inference unless offloading layers If you run out of VRAM, extra system RAM only helps if you offload some model layers to the CPU, which slows performance significantly.

Quantization reduces memory requirements Using 4-bit quantization cuts memory usage by roughly half compared to full precision, making models more accessible on limited hardware.

Context length also consumes memory Longer context windows increase RAM and VRAM usage, so factor that in when choosing your hardware.