Can I run Gemma 3 4B?

Gemma 3 4B is Google's small workhorse for people who want a capable local assistant that also understands images. At 4.3B parameters it punches above the size you expect from a 4B model, and the footprint is tiny: a 4-bit quant is about 2.6 GB, and you only need around 6 GB of RAM to run it. That means it fits on almost anything with a pulse: an old 8 GB laptop GPU, a base Apple Silicon Mac, even a 12 GB RTX 3060 with room to spare. If you want vision plus chat without a heavy machine, this is the obvious starting point.

In daily use it feels quick. On an RTX 3060 you can expect around 117 tokens per second at 4-bit, and on an RTX 4090 it climbs to roughly 329, so replies stream faster than you read on either. An Apple M Max sits around 134 tok/s, and even pure CPU on DDR5 manages about 20, which is usable for short prompts. The 128K context window is real but expensive: fill it completely and total memory climbs to about 15.3 GB, so on a small GPU keep your working context to a few thousand tokens or drop to a smaller quant.

Against Qwen 3 4B, the honest trade is clear: Qwen 3 generally has the edge on reasoning and structured, multi-step tasks, while Gemma 3 4B's standout is vision. Few models this small genuinely read images, and that alone makes it the pick when you need a local multimodal model on modest hardware. If you outgrow it, Gemma 3 12B is the natural step up in the same family. One caveat: the Gemma license is open-weight, not open-source, and carries Google's own usage terms, so read them before shipping it in a product.