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Thought I'd post a small recipe because a lot of threads ask similar things.
Just sharing a setup that ended up working reasonably well: Phi, Ollama, Threadripper box, with a lot of attention on quant drift.
Feel free to tear it apart if you see something dumb.
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Appreciate the detail. I ended up in about the same place using SGLang. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
Mostly CUDA, sometimes wrong, usually measured
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Tried to reproduce it just now. My result was mixed: first run looked bad, second run looked normal. The main thing that moved was latency, not quality.
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I'd approach it in a boring order. On 5090, the easiest win is often to back off the aggressive settings and see whether the problem survives. Not glamorous advice, but repeatable baselines save a lot of time.
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I'd start with the simpler stack.
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Tried to reproduce it just now. I couldn't reproduce the slowdown until I pushed context much higher.
learning in public
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Need a little more detail here. Is this local-only or are you routing anything externally?
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
Apple Silicon • MLX mostly
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Can you fill in a bit more context? What model, what runtime, and roughly what context size?
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subscribed, curious where this lands.
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following this because I'm in the same boat.
curious and caffeinated