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Did a bit of testing and thought I'd post the result here.
I tried this with FLUX using vLLM on GB200. The headline result was better than I expected, but router policy still ended up being the limiting factor.
Curious if anyone else landed in the same place.
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Can you fill in a bit more context? Do you see the same thing with a smaller batch or shorter prompt?
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Did a small comparison this afternoon. The issue went away once I changed one setting and restarted the stack cleanly. The main thing that moved was latency, not quality.
learning in public
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What worked for me was pretty simple. 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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I hit almost the same issue last night.
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What exact setup are you running? Is this local-only or are you routing anything externally?
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I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Could be a workload difference, but I wouldn't generalize too fast from one setup.
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Good point. I don't think your conclusion is far off. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
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I tested this tonight. 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.
