Can I run Gemma 3 12B?

Gemma 3 12B is Google's mid-size open-weight model, and it lands in a useful spot for people who want more capability than the usual 7-8B chat models without jumping to something that needs a workstation. At a 4-bit quant it sits around 7.4 GB, which fits on a 12 GB card like an RTX 3060 with a little headroom, and runs comfortably in unified memory on a 16 GB-plus Apple Silicon Mac. The floor is 12 GB of RAM to load it at all. It also handles vision, so you can feed it images alongside text, which most models this size cannot.

In daily use it feels responsive on consumer hardware. On an RTX 3060 12GB you get around 41 tokens per second at 4-bit, faster than you read, and an Apple M Max pushes that to roughly 47 tok/s. A 4090 will fly at about 116 tok/s if you have one. The 128K context window is the catch: it is genuinely there, but filling it is expensive. At the full 128K the model plus KV cache needs about 27.7 GB total, well past what a 12 GB card holds, so keep working context modest unless you have a 24 GB GPU or plenty of unified memory.

Against Mistral Nemo 12B, the obvious same-size alternative, Gemma 3 12B generally has the edge on instruction-following and multimodal work since Nemo is text-only, though Nemo tends to be the lighter pick for pure long-context chat. If you want to stay smaller, Gemma 3 4B drops the footprint while keeping vision. Gemma 3 12B's standout is being a capable vision-and-chat model that still runs on a single mid-range GPU. One caveat: it ships under Google's Gemma license, which is open-weight but carries provider-specific terms, so read them before any commercial deployment rather than assuming it is plain open source.