Why the NorthCUA 88.64 walkthrough needed a video, not a screenshot

I spent a week trying to explain the NorthCUA run in text and kept failing. The number 88.64 against the board leader is the kind of claim that sounds like a typo in a post, so I recorded a full walkthrough instead: https://www.youtube.com/watch?v=MMzLr50WFxU

The concrete bit people always ask about is the 9B variant. It fits on a single GPU because we run the action-decoding head in int4 while keeping the vision encoder in fp16 — the encoder is what eats the memory, but it only processes the screenshot once per step, so quantizing the decoder is where the savings actually come from. That split is visible in the video at the 6-minute mark, where I dump VRAM usage step by step.

What it costs the operator is real, though: the int4 decoder adds roughly 40ms of latency per action, and in a long browser task those milliseconds compound into noticeable wall-clock time. For batch runs it's fine; for anything interactive, the fp16 9B build is still the better default. I say this in the video too, because a benchmark that hides its tradeoffs is just marketing.

The walkthrough covers the full setup: data pipeline, the eval harness, and the failure cases where NorthCUA still misreads nested menus. If you're evaluating computer-use agents, the failure footage is the part I'd actually watch.