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This keeps coming up in conversation so I figured I'd start a thread.
It feels like the conversation has shifted hard toward inference and serving details lately, and honestly that makes sense once you start dealing with tail latency.
Maybe obvious to some of you, but it clicked for me this week.
For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.
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What worked for me was pretty simple. On Threadripper box, 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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subscribed, curious where this lands.
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I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at prefix caching. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
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What exact setup are you running? Do you see the same thing with a smaller batch or shorter prompt?
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Interesting, I hadn't tried that. I ended up in about the same place using PyTorch. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
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I'd start with the simpler stack.
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Quote:same here on my side.
I don't think that's the whole story. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit. Could be a workload difference, but I wouldn't generalize too fast from one setup.
LoRA tinkerer • dataset janitor