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small win thread on A6000 - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Miscellaneous Discussion (https://www.datasculpting.ai/inference/forumdisplay.php?fid=24) +--- Forum: General (https://www.datasculpting.ai/inference/forumdisplay.php?fid=25) +--- Thread: small win thread on A6000 (/showthread.php?tid=671) Pages:
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small win thread on A6000 - quiettoken - 10-02-2026 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. Re: small win thread on A6000 - edgequill - 10-03-2026 that fixed it for me too. Re: small win thread on A6000 - ggufwire22 - 10-03-2026 Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt? Re: small win thread on A6000 - bytegarden73 - 10-05-2026 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. Re: small win thread on A6000 - plaingarden18 - 10-05-2026 Thanks, that helps. The pattern around reranking has been pretty similar for me. Re: small win thread on A6000 - ggufwire22 - 10-06-2026 that fixed it for me too. I keep promising myself this is the last rebuild. Re: small win thread on A6000 - vectorshed43 - 10-06-2026 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. Re: small win thread on A6000 - sparrowghost - 10-06-2026 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. Re: small win thread on A6000 - localtinker - 10-06-2026 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. Re: small win thread on A6000 - rocmrunner40 - 10-06-2026 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. |