small win thread on A6000

[quiettoken]

Posting this in case it saves someone else a little time.

If you were starting from scratch today for this use case, what would you choose first and what would you ignore?

Simple and boring is a valid answer.

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.

[edgequill]

that fixed it for me too.

[ggufwire22]

Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?

[bytegarden73]

Interesting, I hadn't tried that. I ended up in about the same place using PyTorch. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.

[plaingarden18]

Thanks, that helps. The pattern around reranking has been pretty similar for me.

[ggufwire22]

that fixed it for me too. I keep promising myself this is the last rebuild.

[vectorshed43]

Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.

[sparrowghost]

What worked for me was pretty simple. On mini PC, the easiest win is often to back off the aggressive settings and see whether the problem survives. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.

I'm happy to test one more variant if you want a second data point.

[localtinker]

A couple things I'd check first. If you're on TensorRT-LLM, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time.

[rocmrunner40]

That matches what I was seeing. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.