Our purpose
Why this exists
Understanding the systems behind AI helps more people build, question, and improve them.
ML systems turn model capabilities into something people can use. This site is for the people learning how that happens and sharing what they discover.
Systems make models useful
A capable model is only part of a working product. Memory use, response time, training cost, and reliability matter too. Quantization, batching, caches, kernels, and schedulers help make models practical. We want to make that work easier to understand.
More choice in where AI runs
Some workloads belong in a datacenter. Others benefit from running on a laptop, phone, or device nearby. Local inference can offer privacy, offline access, and lower latency, with real limits on memory and power. Understanding those tradeoffs gives people more control over the systems they use.
Fundamentals outlast the headlines
Models and frameworks change quickly. Memory hierarchies, arithmetic intensity, batching, and the cost of moving data remain useful ways to reason about them. Learning these fundamentals helps you evaluate new ideas instead of starting from scratch each time.
Knowledge grows when we share it
Useful knowledge is scattered across papers, code, talks, and individual experience. A clear explanation or an honest account of a failed approach can save someone else days of work. Publishing it openly makes that experience available beyond one team.
A place to contribute
We bring together articles, primers, and tools from people learning and building ML systems. Everything is free to read, and anyone can submit work for review. If you've learned something worth passing on, there's room for it here.