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Spent some time on this today and wanted to compare notes.
I've gone back and forth between PyTorch and Ollama for Kimi. The interesting difference for me has been router policy.
Interested in tradeoffs more than fan loyalty.
For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.
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Good point. The pattern around tokens/sec has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
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That matches what I was seeing. The pattern around embeddings 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.
curious and caffeinated
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Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.
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What worked for me was pretty simple. On B200, the easiest win is often to back off the aggressive settings and see whether the problem survives. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
team red, team patient
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Thanks, that helps. I ended up in about the same place using llama.cpp. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
rack in the garage • local models everywhere
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What worked for me was pretty simple. Before rebuilding anything, I'd test one clean baseline with Phi and compare from there.
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That matches what I was seeing. That matches what I saw on 7900 XTX. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.