Slightly related, on the cooperation between large models and small models (traditional ML) : https://arxiv.org/abs/2409.06857

A few weeks ago, I processed a product refund with Amazon via agent. It was simple, straightforward, and surprisingly obvious that it was backed by a language model based on how it responded to my frustration about it asking tons of questions. But in the end, it processed my refund without ever connecting me with a human being.

I don't know whether Amazon relies on LLMs or SLMs for this and for similar interactions, but it makes tons of financial sense to use SLMs for narrowly scoped agents. In use cases like customer service, the intelligence behind LLMs is all wasted on the task the agents are trained for.

Wouldn't surprise me if down the road we start suggesting role-specific SLMs rather than general LLMs as both an ethics- and security-risk mitigation too.

One could start with a large model for exploration during development, and then distill it down to a small model that covers the variety of the task and fits on a USB drive. E.g. when I use a model for gardening purposes, I could prune knowledge about other topics.

How is SLM the future of AI while we are not even sure about if LMs are the future of AI?

No mention of mixture-of-exports. Seems related. They do list a DeepSeek R1 distillate as an SLM. The introduction starts with sales pitch. And there's a call-to-action at the end. This seems like marketing with source references sprinkled in.

That said, I also think the "Unix" approach to ML is right. We should see more splits, however currently all these tools rely on great language comprehension. Sure, we might be able to train a model on only English and delegate translation to another model, but that will certainly lose (much needed) color. So if all of these agents will need comprehensive language understanding anyway, to be able to communicate with each other, is SLM really better than MoE?

What I'd love to "distill" out of these models is domain knowledge that is stale anyway. It's great that I can ask Claude to implement a React component, but why does the model that can do taxes so-so also try to write a React component so-so? Perhaps what's needed is a search engine to find agents. Now we're into expensive market place subscription territory, but that's probably viable for companies. It'll create a larger us-them chasm, though and the winner takes it all.

I wonder how the math turns out when we compare the energy use of local vs remote models from first principles.

A server needs energy to build it, house, power and maintain it. It is optimized for throughoutput and can be used 100% of the time. To use the server, additional energy is needed to send packets through the internet.

A local machine needs energy to build and power it. If it lives inside a person's phone or laptop, one could say housing and maintenance is free. It is optimized to have a nice form factor for personal use. It is used maybe 10% of the time or so. No energy for internet packages is needed when using the local machine.

My initial gut feeling is that the server will have way better energy efficiency when it comes to the amount of calculations it can do over its lifetime and how much energy it needs over its lifetime. But I would love to see the actual math.