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simple local serving recipe: setup advice - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: LLM Models (https://www.datasculpting.ai/inference/forumdisplay.php?fid=12) +--- Forum: Open Weight (https://www.datasculpting.ai/inference/forumdisplay.php?fid=49) +---- Forum: Local (https://www.datasculpting.ai/inference/forumdisplay.php?fid=51) +---- Thread: simple local serving recipe: setup advice (/showthread.php?tid=200) Pages:
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simple local serving recipe: setup advice - mlxgarden - 09-10-2026 Thought I'd post a small recipe because a lot of threads ask similar things. This isn't revolutionary, but the combination of MLX and A6000 felt surprisingly usable once I tuned around quant drift. Maybe useful as a starting point for newer folks. Re: simple local serving recipe: setup advice - cacheline - 09-12-2026 Can you fill in a bit more context? Do you see the same thing with a smaller batch or shorter prompt? Re: simple local serving recipe: setup advice - llmpeak - 09-12-2026 Ran a quick check on my box. The issue went away once I changed one setting and restarted the stack cleanly. Re: simple local serving recipe: setup advice - vramstream - 09-12-2026 My first guess would be this: If you're on PyTorch, I'd confirm the version first and then reduce variables until the behavior is obvious. Re: simple local serving recipe: setup advice - cachebench - 09-12-2026 Can you fill in a bit more context? Is this local-only or are you routing anything externally? Re: simple local serving recipe: setup advice - rocmriley - 09-14-2026 that fixed it for me too. Re: simple local serving recipe: setup advice - rasterbench - 09-14-2026 What exact setup are you running? Do you see the same thing with a smaller batch or shorter prompt? Re: simple local serving recipe: setup advice - mintstation99 - 09-14-2026 I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at embeddings. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. Re: simple local serving recipe: setup advice - rocmriley - 09-17-2026 I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Could be a workload difference, but I wouldn't generalize too fast from one setup. Re: simple local serving recipe: setup advice - audioriver - 09-18-2026 Can you fill in a bit more context? Do you see the same thing with a smaller batch or shorter prompt? |