> applying this compression algorithm at scale may significantly relax the memory bottleneck issue.

I don’t think they’re going to downsize though, I think the big players are just going to use the freed up memory for more workflows or larger models because the big players want to scale up. It’s a cat and mouse race for the best models.

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miohtama 5 days ago | parent | next [-]

It will also help with local inference, making AI without big players possible.

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otabdeveloper4 5 days ago | parent [-]

It's already possible. Post-training is vastly more important than model size. (There's bigtime diminishing returns with increasing model size.)

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plagiarist 5 days ago | parent [-]

Is there a size cutoff you would say where diminishing returns really kick in?

My experience doesn't disagree, at least. I've been using Qwen for coding locally a bit. It is much better than I thought it would be. But also still falls short in some obvious ways compared to the frontiers.

	▲	otabdeveloper4 4 days ago \| parent [-]
		> Is there a size cutoff you would say where diminishing returns really kick in? No idea yet. But also it's obvious that making LLMs without MoE is stupid.

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Verdex 5 days ago | parent | prev [-]

Known in the business as 'pulling a jevons'