4 Reasons 32GB RAM Is Worth It for Local LLMs

7B models need 5–6GB just for the model A 4-bit quantized Llama 2 7B uses about 5–6GB of RAM. That leaves little room for anything else on a 16GB system.

Context windows eat memory fast Longer conversations with an LLM consume more RAM for the context window. 16GB forces swapping to disk during decent chat lengths, while 32GB gives you breathing room.

16GB works but you will hit limits You can run a local LLM on 16GB, but you will be closing apps and fighting slowdowns. 32GB means you do not have to sacrifice your browser or other tools.

RAM is for loading, not speed System RAM handles loading the model and context, but inference speed depends on your GPU or CPU compute. A GPU with 8–12GB VRAM matters more than system RAM for fast token generation.