I thought 16GB of VRAM was overkill until I started automating my work with local LLMs

This cache stores the attention state for every token in your context, and it is stored in your VRAM right alongside the model weights. It checks that critical components like RAM, CPU, and GPU are present and functional. Try AgainMultiple models eat up VRAM fasterIn a real-world pipeline, automation rarely means running one model in isolation. Surprisingly smart local AIGenerally, 8GB runs a 7B model, 12GB handles 14B, and 16GB gets you into the 24B range. 16GB of VRAM with 32GB of system RAM is a very capable gaming setup, but it no longer feels excessive.