Previewing GPT‑5.6 Sol: a next-generation model

> Things like continuing education so your model knows about the latest NPM packages or world news is super important, but seems like it would require new chips.

They probably have a few ideas around that. Me, personally, I'd have one main expensive chip (replaced every 10 years, or whatever), with a secondary cheap chip in front of it that gets replaced every year or so.

The secondary chip could act the way RAG does, or perhaps both chips together can act as LoRA.

Either way, 99.999% of the knowledge is static, you just need to fine-tune the weights with that remaining 0.001% knowledge, which can be done using RAG or LoRA on a much smaller (thus cheaper) disposable chip.

the flash models have fallen in size at least between deep seek models. Is there a limit to the shrinking capacity of the models?

> They expect to scale up to real LLMs by splitting a model layer-wise across several chips, which they can do without incurring any throughput penalty.

I’ll patiently wait to see this in reality. Their demonstration hardware is a 250W chip that is enormous in die area for the model size. They’re making a lot of claims, but until they can deliver then it’s nearly vaporware in my view.

I’d be happy to be proven wrong, but I think they’re going to quickly run into hardware realities quite soon if they think they can just chain a bunch of chips together to achieve the same performance on larger sizes.

But I’m not missing the point. If you can run one frontier model at 750t/s, then you can probably run many many instances of an SLM in parallel at a rate that exceeds 15k/s. That’s kinda the point of the flash or ultrafast variants. And they’re on something much more modern than llama3.1.