Napkin Math

The goal of this project is to collect software, numbers, and techniques to quickly estimate the expected performance of systems from first-principles. For example, how quickly can you read 1 GB of memory? By composing these resources you should be able to answer interesting questions like: how much storage cost should you expect to pay for logging for an application with 100,000 RPS?

The best introduction to this skill is through my talk at SRECON.

The best way to practise napkin math in the grand domain of computers is to work on your own problems. The second-best is to subscribe to this newsletter where you'll get a problem every few weeks to practise on. It should only take you a few minutes to solve each one as your facility with these techniques improve.

The archive of problems to practise with are here. The solution will be in the following newsletter.

Numbers

Below are numbers rounded for memorization, not faux precision. Last measured in October-November 2026, averaged over GCP C4 and AWS m8i (x86) and m8g (Graviton4), 48 vCPU each, and their local-SSD variants; network disks on Hyperdisk, EBS and pd-standard, HDDs on an AWS d3. Blob storage was measured in March 2026, and the GPU rows are NVIDIA's specs. Most cells are one run per machine, so trust the order of magnitude more than the digit. See BENCHMARKING.md to run them yourself.

This is the beginning of the README. Read the rest at the original. Read the full README ↗