Design a Distributed AI Model Downloader

Asked by: Anthropic

"Design a distributed AI model downloader" is an Anthropic system-design question that sounds like plumbing and is actually one of the better tests of distributed-systems instinct. The job: get a multi-hundred-gigabyte set of model weights onto thousands of GPU nodes quickly and reliably, especially during the worst case — a cold autoscale event where a thousand nodes all need the same weights right now. It fits the standard interview framework, so we'll use it.

The crux (spend ~60% of your time here). The naive design — every node pulls the model from object storage — saturates the storage egress and the network the instant the fleet is large, and download time grows with fleet size. The entire problem is how to make distribution scale with the fleet instead of against it (peer-to-peer / tree multicast), plus content-addressed chunking for integrity, dedup, and resume. Object storage as the source of truth is table-stakes; the distribution topology is the interview.