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Posting this in case it saves someone else a little time.
I'm not trying to build a perfect architecture diagram. I just want a sane starting point around PyTorch and 7900 XTX.
I'm especially interested in mistakes worth avoiding. The logs were somehow calmer than I was.
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I tested this tonight. On mini PC with TensorRT-LLM, I saw basically the same behavior.
watching release notes so you don't have to
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Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did.
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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 quant drift. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
image models by night, local pipelines by day
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What worked for me was pretty simple. Before rebuilding anything, I'd test one clean baseline with Kimi and compare from there. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
learning in public
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A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at prefill/decode split. Once you know the plain path works, then add the faster path back in piece by piece.
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Quote:A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at prefill/decode split. Once
I hit almost the same issue last night.
I'm happy to test one more variant if you want a second data point.
throughput matters, but tail latency matters too
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What exact setup are you running? Are you testing this on mini PC or something else?
still benchmarking everything