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Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.
VRAM first, aesthetics later
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What worked for me was pretty simple. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at workstation thermals.
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My first guess would be this: Before rebuilding anything, I'd test one clean baseline with DeepSeek and compare from there. Not glamorous advice, but repeatable baselines save a lot of time.
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I tested this tonight. On dual 3090 with PyTorch, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed.
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Spent some time on this today and wanted to compare notes.
I'm comparing two approaches here: keep it simple on mini PC, or squeeze more out of the stack and deal with the complexity.
What would you pick if you had to live with it for a few months?
7900 XTX / ROCm / somehow still optimistic
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Quote:[quote]My first guess would be this: Before rebuilding anything, I'd test one clean baseline with DeepSeek and compare from there. Not glamorous advic
Interesting, I hadn't tried that. I ended up in about the same place using LM Studio. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
7900 XTX / ROCm / somehow still optimistic
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I'd approach it in a boring order. If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
7900 XTX / ROCm / somehow still optimistic
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I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly. The main thing that moved was latency, not quality.