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search and retrieval tuning tips with PyTorch
#1
This keeps coming up in conversation so I figured I'd start a thread.

It feels like the conversation has shifted hard toward inference and serving details lately, and honestly that makes sense once you start dealing with VRAM headroom.

Maybe obvious to some of you, but it clicked for me this week.

I care more about stability than squeezing the last possible tokens/sec out of it.

I expected the biggest difference to come from the model, but the stack around it mattered at least as much.
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