+- Inference (https://www.datasculpting.ai/inference)
+-- Forum: Security, Privacy & Safety (https://www.datasculpting.ai/inference/forumdisplay.php?fid=23)
+--- Forum: Local Privacy (https://www.datasculpting.ai/inference/forumdisplay.php?fid=76)
+--- Thread: privacy wins of local models: what are you seeing? (/showthread.php?tid=491)
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Re: privacy wins of local models: what are you seeing? - rasterbench - 10-02-2026
Quote:Not sure I agree there. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit. Not saying you're wrong, just t
What exact setup are you running? Are you testing this on A6000 or something else?
Re: privacy wins of local models: what are you seeing? - cachebench - 10-03-2026
that fixed it for me too.
Re: privacy wins of local models: what are you seeing? - rocmhouse90 - 10-05-2026
My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at MoE serving. Once you know the plain path works, then add the faster path back in piece by piece.
Re: privacy wins of local models: what are you seeing? - audioriver - 10-05-2026
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. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
Re: privacy wins of local models: what are you seeing? - mangoquill - 10-05-2026
Quote: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. A l
Appreciate the detail. I ended up in about the same place using SGLang. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
Re: privacy wins of local models: what are you seeing? - rasterbench - 10-05-2026
What worked for me was pretty simple. If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious.