> malicious

It doesn't have to be malicious. If my workflow is to send a prompt once and hopefully accept the result, then degradation matters a lot. If degradation is causing me to silently get worse code output on some of my commits it matters to me.

I care about -expected- performance when picking which model to use, not optimal benchmark performance.

The question I have now after reading this paper (which was really insightful) is do the models really get worse under load, or do they just have a higher variance? It seems like the latter is what we should expect, not it getting worse, but absent load data we can't really know.

Explain this though. The code is deterministic, even if it relies on pseudo random number generation. It doesn't just happen, someone has to make a conscious decision to force a different code path (or model) if the system is loaded.

It's very clearly a cost tradeoff that they control and that should be measured.

The primary (non malicious, non stupid) explanation given here is batching. But I think you would find looking at large-scale inference the batch sizes being ran on any given rig are fairly static - there is a sweet spot for any given model part ran individually between memory consumption and GPU utilization, and generally GPUs do badly at job parallelism.

I think the more likely explanation is again with the extremely heterogeneous compute platforms they run on.