frontier API value lately

[routecraft]

Did a bit of testing and thought I'd post the result here.

After a few runs on 4090, I ended up with decent performance, but the setup only really made sense once I adjusted around tail latency.

Happy to post more details if anyone wants them.

My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.

[routecraft]

Tried to reproduce it just now. The issue went away once I changed one setting and restarted the stack cleanly. I expected the model choice to matter more than it did.

[relaynest82]

My first guess would be this: Before rebuilding anything, I'd test one clean baseline with Gemma and compare from there. Not glamorous advice, but repeatable baselines save a lot of time. Turns out 'just one more test' was a lie.

[routecraft]

I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at KV cache pressure. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.

[routecraft]

A couple things I'd check first. On B200, the easiest win is often to back off the aggressive settings and see whether the problem survives. Once you know the plain path works, then add the faster path back in piece by piece.

[rocmriley]

Appreciate the detail. I ended up in about the same place using llama.cpp. The biggest change for me was simplifying the stack instead of adding one more optimization layer.

[plainscope50]

That matches what I was seeing. That matches what I saw on 4090. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.

[routecraft]

That matches what I was seeing. That matches what I saw on 4090. Worth rechecking your defaults though, because one bad assumption can make the rest o

Good point. That matches what I saw on 4090. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.

[routecraft]

Good point. I ended up in about the same place using PyTorch. The biggest change for me was simplifying the stack instead of adding one more optimization layer.