I do think these “run a bigger model than will fit in VRAM” projects are necessary steps, but are they functionally useful or helpful to anyone currently? For example, is anyone out there running a big Qwen for coding on a 16-32GB machine with these techniques?

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dsl 22 minutes ago | parent | next [-]

> but are they functionally useful or helpful to anyone currently?

I've been building a SaaS that deals with data that can't be distributed to third parties. Some of the useful AI stuff I can add is not time sensitive and can run overnight. Things like this allow me to use higher quality models without selling my house for GPUs.

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pizza234 7 hours ago | parent | prev | next [-]

> but are they functionally useful or helpful to anyone currently?

Yes and no, depend on your expectations. Some/many like to run local LLMs just for the sake of it, so anything will do.

MoE are useful on PC systems, at the condition of having high enough memory bandwidth (and large amounts of RAM) - that is, Threadripper/Pro.

The advantage of MoE is that only a subset of the model's experts is used for each token, so not all weights need to be present in VRAM at once. The remaining weights can reside in system RAM, although moving and accessing them still carries a substantial performance cost (and that's why high memory bandwidth is needed).

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ddevnyc 6 hours ago | parent | next [-]

Does MoE help with multimodality? Can it in general enable reasoning in imagery (technical drawings, diagrams, schematics) rather than text-based?

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dannyw 6 hours ago | parent [-]

MoE has nothing to do with multimodality.

MoE is a concept proposed in 1991, before the deep learning era (which is before what I call the transformers era). You can think of it like sharing.

Contrary to popular belief; 'experts' in MoE LLMs do not specialize. There's no expert trained to be good at maths, or python, or writing, or whatever. It's an inference optimization.

As for reasoning in non-text modalities, you might find this paper interesting :) https://huggingface.co/papers/2502.05171

	▲	dummydummy1234 4 hours ago \| parent \| next [-]
		Wait I thought the router ends up specializing the experts? Like there is no explicit goal aside from each 'expert' getting roughly equal weight? And it happens that when you train the router you do end up passing certain classes of problem to each expert - just as a training result nothing as clean as a python expert. But math vs creative writing will tend to rely on different experts over the majority of the inference? I do not know what I am talking about, this is my limited understanding...
	▲	ahepp an hour ago \| parent \| prev [-]
		> You can think of it like sharing. was this meant to read "sharding"?

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bensyverson 6 hours ago | parent | prev [-]

Are people getting decent tokens/second throughput? Some of these demos crawl at 1 tok/s or worse, which limits their utility.

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reactordev 6 hours ago | parent | next [-]

yes, I average 80-120 tok/s on my RTX 3080 with gemma 4 and faster with Qwen 3.5. The main use-case here is just code-monkey agents. I'm not looking for architectural guidance, but an agent to take a spec and complete it.

	▲	bensyverson 6 hours ago \| parent [-]
		And is this using conventional model loading (all in VRAM), or are you streaming it in some way?

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cyanydeez 2 hours ago | parent | prev [-]

in theory, QWEN3.6-35B-A3B should run as fast as a 3B model; and in practice, it can be just as dumb.

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4 hours ago | parent | prev | next [-]

[deleted]

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zapataband1 40 minutes ago | parent | prev | next [-]

I don't need any of these to help with coding. skill issue.

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nickpsecurity 6 hours ago | parent | prev [-]

If I could justify wear and tear and electricity, I was willing to do something like this for batch processing. The batches would be a bunch of prompts whose outputs I'd look at the next day. Maybe common operations, like QA or refactoring, on whatever software I wrote.

If so, I could use a larger model than I have real-time hardware for. The largest, well-trained models can often get the output mostly right in one try. I also would be using AI's as a supplement to, not replacement for, my own brain. So, issues with the outputs wouldn't be a problem because I'm just keeping what's helpful.

If I still need to re-generate it all, it might still save money over time by avoiding cloud costs. Also, hardware that's already paid for is a sunk cost that doesn't inflate over time. Glitches in loading or destroying VM's might blow up into a big bill.

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parineum 4 hours ago | parent [-]

at 5 minutes per token, you could look at the results next week

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bensyverson 4 hours ago | parent [-]

one week later: "It says 'You're absolutely right! Let me look at the seams so I'm checking, not guessing—' and I guess that's when my SSD melted."

	▲	hex4def6 32 minutes ago \| parent [-]
		Turns out the SSD was load-bearing.