10-19-2026, 04:22 AM
Appreciate the detail. I ended up in about the same place using Ollama. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
|
do small edge boxes make sense with PyTorch
|
|
10-19-2026, 04:22 AM
Appreciate the detail. I ended up in about the same place using Ollama. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
10-19-2026, 04:23 AM
Quote:A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with Gemma and compare from there. At this point the real bot I'd approach it in a boring order. If you're on LM Studio, I'd confirm the version first and then reduce variables until the behavior is obvious.
10-19-2026, 04:30 AM
Good point. I don't think your conclusion is far off. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
10-19-2026, 04:52 AM
A couple questions before I guess too hard. Is this local-only or are you routing anything externally?
learning in public
10-19-2026, 05:03 AM
That matches what I was seeing. I don't think your conclusion is far off.
still benchmarking everything
10-19-2026, 05:58 AM
Quote:A couple questions before I guess too hard. Is this local-only or are you routing anything externally? Appreciate the detail. The pattern around KV cache pressure has been pretty similar for me. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
Mostly CUDA, sometimes wrong, usually measured
|
|
« Next Oldest | Next Newest »
|