I'd assume the closed weight models are all working on shrinking their parameter counts anyways. They too benefit from smaller models. It'd be foolish for these SOTA labs to not be working at reducing parameter counts.

I would say even without rampocalypse there would still be the strong incentive to innovate at the edge and under more extreme constraints. The incentives are just even stronger now.

I'm looking forward to seeing what types of new things people create over the coming years once there is less obsession with massive unwieldy LLMs. I think the incentives are just too strong to ignore.

This is just temporary though, right?

With the benefit of LLMs already being proven, in a couple of years we will have vastly better hardware for inference I guess.

I feel like now hardware is stagnating a bit, because the software side has moved too fast for the hardware to catch up. Once we settle on some good, optimal software architecture for the models, dedicated hardware will easily increase throughout by 10x or 100x, for a fraction of the cost.

LLMs seems quite simple, maybe we'll be able to print/assemble at home our own chips with the desired models/weights.

Maybe we'll have model weights being shared like game cartridges.

I am literally betting my company on this being true.

I hope ssd streaming gets more popular, maybe more breakthroughs like that will help change things

I think the path of least resistance will end up being the cheapest and that is scaling up the parameters a ridiculous amount until you get highly capable models that can develop/distill/design the RAM efficient models. Going straight for low param is foolish and just a cope by smaller labs because they don't have the compute/talent to train the large ones.

This is 100% true for pretrains, likely true for RL as well although maybe there is some benefit to smaller activated params there. There is of course 0 benefit to small dense models relative to large sparse ones that are equally as memory efficient if you have enough computers.

Many on HN are in deep denial about this imo.

Right now there is a stark difference between what smaller models can do and bigger models can do.

Smaller models are suitable for simple tasks like classification / summarisation while larger models are better in agentic capabilities.

Let's not forget the Bitter Lesson. Small models sound really nice but at some point you're just fighting the laws of information theory. Efficiency gains on the small model side are nice, but efficiency gains + giant model tends to be even better...

I honestly hope to see this across all applications, games, services, operating systems, etc. We've been in a period of wasteful RAM usage for over a decade. Constraints, whatever their origin, can be a good thing.

Really? I feel like because nobody has RAM they're being pushed to the cloud frontier models. If we could all have our own 64GB+ VRAM GPUs, I feel like the open weight model scene would be even stronger.