Ten days ago I had a benchmark: reorder the expert weights inside a MoE model file by measured co-activation, and disk reads per token drop 2.23×. It was real, reproducible, and measured entirely on my own machine — which is to say, it was worth almost nothing yet.
Today the strongest number attached to this work is one I didn't produce: +32.3% decode throughput and −26.3% time-to-first-token on a 235B-parameter model running on a 48 GB MacBook, measured by someone I've never met, on an inference engine I'd never run, using my script unmodified, with the arms swapped as a page-cache control. Along the way, two of my three original pitches were refuted with data, my one pre-registered scaling prediction failed its own threshold, and both of my attempts to forecast a key parameter missed in opposite directions.
The refutations were the productive part. This post is about the process that made them cheap: five engines, a half-dozen strangers, one issue thread, and the discipline that emerged in it — because I think the process generalizes and the write-ups I usually see don't mention it.
The setup
The idea (project: mbolt) is BOLT/PGO applied to model binaries. Checkpoint tensor order is an accident of the training pipeline; MoE routing at inference time is far from random — experts fire in cliques. If an engine streams experts from SSD, the file layout decides whether a token's misses are a handful of long sequential reads or thousands of scattered ones. So: trace the routing, cluster the co-activation, rewrite the file, keep the weights byte-exact.
