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vision-language models on consumer gear - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Generative AI (https://www.datasculpting.ai/inference/forumdisplay.php?fid=17) +--- Forum: Video (https://www.datasculpting.ai/inference/forumdisplay.php?fid=70) +--- Thread: vision-language models on consumer gear (/showthread.php?tid=1042) Pages:
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vision-language models on consumer gear - modrunner27 - 10-11-2026 Thought I'd post a small recipe because a lot of threads ask similar things. Just sharing a setup that ended up working reasonably well: DeepSeek, vLLM, A6000, with a lot of attention on reranking. Maybe useful as a starting point for newer folks. My use case is pretty ordinary: local chat, some document work, and occasional batch jobs. I expected the biggest difference to come from the model, but the stack around it mattered at least as much. Re: vision-language models on consumer gear - quietarch - 10-12-2026 Quote:Thought I'd post a small recipe because a lot of threads ask similar things. A couple things I'd check first. If you're on MLX, I'd confirm the version first and then reduce variables until the behavior is obvious. Turns out 'just one more test' was a lie. Re: vision-language models on consumer gear - visionforge - 10-13-2026 Did a small comparison this afternoon. The issue went away once I changed one setting and restarted the stack cleanly. I expected the model choice to matter more than it did. Re: vision-language models on consumer gear - quietarch - 10-13-2026 My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at prefix caching. Re: vision-language models on consumer gear - oxideworks59 - 10-13-2026 My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at VRAM headroom. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. Re: vision-language models on consumer gear - quietarch - 10-13-2026 I don't think that's the whole story. I've seen people blame structured output when the real issue was much more mundane. The logs were somehow calmer than I was. Re: vision-language models on consumer gear - visionforge - 10-14-2026 Quote:I don't think that's the whole story. I've seen people blame structured output when the real issue was much more mundane. The logs were somehow calmer Tried to reproduce it just now. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality. Re: vision-language models on consumer gear - forgemixer - 10-14-2026 that fixed it for me too. Re: vision-language models on consumer gear - audiopath66 - 10-14-2026 that fixed it for me too. Re: vision-language models on consumer gear - forgemixer - 10-14-2026 Interesting, I hadn't tried that. That matches what I saw on GB200. The biggest change for me was simplifying the stack instead of adding one more optimization layer. |