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Search / Retrieval / Reranking discussion with PyTorch
#11
Quote:Appreciate the detail. I ended up in about the same place using llama.cpp. I got better results once I stopped chasing one metric and just looked at w

Ran a quick check on my box. On M3 Max with TensorRT-LLM, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed.
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#12
My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at HBM constraints. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
throughput matters, but tail latency matters too
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#13
Quote:My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at HBM constraints. A lot of the

I hit almost the same issue last night.
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#14
Good point. I ended up in about the same place using MLX. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
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#15
Interesting, I hadn't tried that. The pattern around reranking has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
learning in public
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