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Another reason to self host your own AI (aussie.zone)

submitted 2 weeks ago* (last edited 2 weeks ago) by SuspiciousCarrot78@aussie.zone to c/selfhosted@lemmy.world

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Recent post re: AI as utility

https://www.tomsguide.com/ai/people-will-buy-intelligence-from-us-on-a-meter-chatgpts-ceo-sam-altman-has-critics-worried-with-his-ai-vision

Myself, I'm a fan of local LLM / self hosted ML.... but if you ever needed a clarion call that a hard pivot is coming (soon) for online/ cloud based AI...Altman et al are making some concerning mouth noises (to say nothing of broader concerns with OAI, Anthropic etc).

Right now, I'm sketching out a plan where my Raspberry Pi (always on, 2-3w) uses a magic packet to wake up my modest AI server (Lenovo P330 with Tesla P4) if/when needed (Qwen 3.6-35B-A3B); no point in chugging down 80-100w, 24/7 for no good reason.

If the trend continues the direction it appears to be (increasing costs, environmental impacts etc) then I'd feel a lot better hosting my own as port of first call and replacing simpler tasks with more traditional programs. YMMV.

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[–] SuspiciousCarrot78@aussie.zone 6 points 2 weeks ago (1 children)

There's an argument to be had regarding a MoE versus a small dense model. I guess it depends on what exactly you need doing with it. I would be tempted to run a smaller dense model (like a Qwen 3-14B or a Qwen 3.5 9B) as at a reasonable quant, it might fit mostly or entirely on the GPU, thereby giving you excellent speeds.

PS: I'm actually in the process of designing an expert system (not a LLM) for pretty much the task you described. The intention is that you would still interact with it like a large language model, but the actual brains underneath it would be something more traditional.

[–] brucethemoose@lemmy.world 1 points 2 weeks ago* (last edited 2 weeks ago)

MoEs can be very fast with hybrid inference. I run Xiaomi Mimo 2.5 (a 310B model, 116GB weights) on my single 3090 + 7800 CPU, and it outputs faster than I can read it.

It's also easier to fit long context, if you need that.

It's best to use the ik_llama.cpp fork for that, though. It gives a huge boost to hybrid MoE speeds.