Smollm3: Smol, multilingual, long-context reasoner LLM

It's small (3B) and does great on benchmarks. This is a model for edge / mobile deployments so the gains over gemma3-4b are meaningful. It has dual mode reasoning / non_reasoning AND they released the full training method:

> We're releasing SmolLM3 with our engineering blueprint. It includes architecture details, exact data mixtures showing how we progressively boost performance across domains in a three-stage pretraining approach, and the methodology for building a hybrid reasoning model. Usually, achieving these results would require months of reverse engineering. Instead, we're providing the full methodology.

I hate to say it, but reasoning models simply aren't suited for edge computing. I just ran some tests on this model and even at 4bit weight quantisation it blows past 10GB of VRAM with just ~1000 tokens while it is still reasoning. So even if you're running on a dedicated ML edge device like a $250 Jetson, you will run out of memory before the model even formulates a real answer. You'll need a high end GPU to make full use of it for limited answers and an enterprise grade system to support longer contexts. And with reasoning turned off I don't see any meaningful improvement over older models.

So this is primarily great for enterprises who want to do on-prem with limited budgets and maybe high-end enthusiasts.