AI / A CONCEPT NOTE

GPUs & VRAM

why AI needs special hardware

~65 sec read

Overview · mechanism
pitfall · examples

01 / THE SHORT VERSION

The idea in a few sentences.

GPUs handle parallel matrix math equations. Graphics Memory (VRAM) stores active model parameters, dictating the maximum model size you can run.

02 / FOLLOW THE MECHANISM

Model load
inference engine parses model file parameters weights.
VRAM allocation
allocates VRAM blocks (e.g. 16GB) to hold model parameter arrays.
Matrix math
attention calculations execute in parallel across thousands of GPU cores.
Overflow swap
if model size exceeds VRAM, parameters spill to slow system RAM, degrading speeds.

04 / COMMAND NOTES

Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.

EXAMPLE 01 · REFERENCE

check GPU resource load and VRAM consumption in Linux

nvidia-smi