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Spent some time on this today and wanted to compare notes.
Two paths both seem viable, but they break down differently once consumer GPUs becomes important.
Trying to avoid rebuilding the whole thing twice.
image models by night, local pipelines by day
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A couple things I'd check first. On mini PC, 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.
image models by night, local pipelines by day
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Good point. I ended up in about the same place using TensorRT-LLM. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
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Interesting, I hadn't tried that. I ended up in about the same place using Ollama. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
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worth checking the driver first.
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
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I think the bottleneck is somewhere else. 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.
rack in the garage • local models everywhere
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Quote:I think the bottleneck is somewhere else. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit. Could be a wo
I tested this tonight. My result was mixed: first run looked bad, second run looked normal. The surprise was that memory pressure showed up before throughput collapsed.
image models by night, local pipelines by day
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A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with DeepSeek and compare from there. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
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A couple things I'd check first. On Threadripper box, the easiest win is often to back off the aggressive settings and see whether the problem survives.
retrieval before vibes