Can I run Gemma 4 E2B?

Gemma 4 E2B is Google's small mixture-of-experts model, and the interesting part is the math: it carries 5.1B parameters total but only activates about 2.3B per token. That means it runs at the speed of a much smaller model while still needing memory for the whole thing. A 4-bit quant lands around 3.1 GB, and you'll want roughly 6 GB of RAM minimum to hold the full set of weights. That fits an entry-level 8 GB GPU or any Apple Silicon Mac with room to spare, and it handles both chat and vision, so you can feed it images, not just text.

In daily use the active-parameter trick pays off in raw throughput. On an RTX 3060 12GB it pushes around 219 tokens per second at 4-bit, and an RTX 4090 takes that past 600; an M-series Max sits near 250. Replies stream far faster than you read. The 128K context is the catch to watch: fill it and total memory climbs to roughly 16.8 GB, well past the 6 GB you need at short context. On a small card, keep working context to a few thousand tokens or the KV cache will outgrow the model itself.

Against its own family, Gemma 3 4B is the dense alternative if you'd rather not deal with the MoE memory footprint, and Qwen 3 4B generally has the edge on multi-step reasoning since that's its stated focus. Gemma 4 E2B's standout is the speed-to-capability ratio plus native vision in a package this light, which is rare at this size. It's Apache 2.0, so unlike Google's older Gemma terms you can use it commercially in production with no special license to read. Pull it with ollama run gemma4:e2b and you're set.