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Posting this in case it saves someone else a little time.
I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about token cost.
I'm especially interested in mistakes worth avoiding.
team red, team patient
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Appreciate the detail. That matches what I saw on GB200. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
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Quote:Appreciate the detail. That matches what I saw on GB200. The biggest change for me was simplifying the stack instead of adding one more optimization l
I'd approach it in a boring order. On GB200, 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.
7900 XTX / ROCm / somehow still optimistic
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Good point. That matches what I saw on M3 Max. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
If you post your exact settings, people here can probably narrow it down pretty fast.
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I'd start with the simpler stack.
still benchmarking everything
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My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at VRAM headroom.
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Tried to reproduce it just now. The issue went away once I changed one setting and restarted the stack cleanly. The surprise was that memory pressure showed up before throughput collapsed.
still benchmarking everything
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What exact setup are you running? What model, what runtime, and roughly what context size?
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Tried to reproduce it just now. On dual 3090 with TensorRT-LLM, I saw basically the same behavior. I expected the model choice to matter more than it did.