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+---- Thread: model fallback rules that work (/showthread.php?tid=968)
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model fallback rules that work - dockember - 10-09-2026
This keeps coming up in conversation so I figured I'd start a thread.
The more I work with local setups, the more it seems like consumer GPUs decides everything downstream.
Curious whether that lines up with what other people are seeing.
Re: model fallback rules that work - kernelmike - 10-10-2026
A couple questions before I guess too hard. Do you see the same thing with a smaller batch or shorter prompt?
Re: model fallback rules that work - llmarch51 - 10-13-2026
Good point. That matches what I saw on GB200.
Re: model fallback rules that work - sparrowhall - 10-13-2026
Maybe, but I had the opposite result. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit.
Re: model fallback rules that work - cozywire - 10-14-2026
I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at tool calling.
Re: model fallback rules that work - kernelpeak - 10-15-2026
A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at KV cache pressure.
Re: model fallback rules that work - batchshed - 10-15-2026
Not sure I agree there. I get why that seems right, but on Threadripper box the bottleneck felt elsewhere.
Re: model fallback rules that work - vectorpoint - 10-15-2026
Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher.
Re: model fallback rules that work - cacheplane98 - 10-18-2026
Good point. I ended up in about the same place using TensorRT-LLM.
Re: model fallback rules that work - batchsize - 10-18-2026
Quote:Good point. I ended up in about the same place using TensorRT-LLM.
Maybe, but I had the opposite result. I get why that seems right, but on 7900 XTX the bottleneck felt elsewhere. Worth testing the simpler explanation before adopting the more interesting one.