comparing models without fooling yourself

[griffinloom66]

Thanks, that helps. The pattern around structured output has been pretty similar for me. I got better results once I stopped chasing one metric and ju

I'd approach it in a boring order. If you're on PyTorch, I'd confirm the version first and then reduce variables until the behavior is obvious. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.

[cacheline]

[quote]Thanks, that helps. The pattern around structured output has been pretty similar for me. I got better results once I stopped chasing one metric

I think the bottleneck is somewhere else. I've seen people blame prefix caching when the real issue was much more mundane. Could be a workload difference, but I wouldn't generalize too fast from one setup.

[benchplease]

What worked for me was pretty simple. On GB200, the easiest win is often to back off the aggressive settings and see whether the problem survives.

[cobaltworks25]

What worked for me was pretty simple. On GB200, the easiest win is often to back off the aggressive settings and see whether the problem survives.

Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did.

[circuitworks90]

I tested this tonight. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality.

[deltastation]

I tested this tonight. My result was mixed: first run looked bad, second run looked normal.