← All resources

GPU Memory Fundamentals

Auto-discovered

Documentation Beginner

Visit resource ↗

Description

GPU Memory Fundamentals is a markdown tutorial in the cverrier/tinygrad-tutos GitHub repository on how GPU memory movement limits performance, with tinygrad examples pinned to a specific commit.

It covers the read-compute-write loop, VRAM versus cores versus the memory interface, and Bandwidth = Memory Speed x Interface Width. Later sections contrast memory-bound vector addition with compute-bound matrix multiply, introduce arithmetic intensity, and show fair tinygrad benchmarking with realize, TinyJit, warmup, and hardware counters.

It targets beginners who want intuition for bandwidth bottlenecks before optimizing kernels. Basic Python and tensor library familiarity help.

The lesson is free as a single GitHub markdown file under the tutos folder, meant to be read with the matching tinygrad checkout.

It gives a concrete mental model for deciding whether GPU work will wait on memory or on compute.