Can I run Gemma 3n E4B?

Gemma 3n E4B is Google's small multimodal model built for chat and vision, and it is aimed squarely at people who want a capable local assistant that can also look at images. On paper it carries 7.8B parameters, but it is a mixture-of-experts design: only about 4B activate per token, so it runs noticeably faster than its full size suggests. At 4-bit it lands around 4.7 GB, which fits an 8 GB GPU like an RTX 3060 with room to spare, and sits easily in unified memory on any recent Apple Silicon Mac. The catch with MoE is that you still need to hold the whole model in memory, so plan for at least 8 GB of RAM regardless of how few experts fire.

Day to day it feels quick. On an RTX 3060 12GB you can expect around 126 tokens per second at 4-bit, and a 4090 pushes that to roughly 353, both well past reading speed; an M-series Max sits near 144, and even pure CPU on DDR5 manages about 21, usable if you are not in a hurry. The vision side is the real reason to reach for it over a text-only model this size. Context tops out at 32K, which is comfortable for chat and document Q&A but not a long-context workhorse. Fill that window and total memory climbs to roughly 8.8 GB, so on an 8 GB card keep working context modest.

Against Llama 3.1 8B, the obvious text-only alternative in its class, Gemma 3n E4B generally trades some raw reasoning depth for two things Llama cannot offer at this footprint: native image understanding and the MoE speed advantage. If you only need text, Gemma 3 4B is the lighter sibling and Llama 3.1 8B the safer pure-chat pick. Gemma 3n E4B's standout is being a genuinely usable vision-and-chat model that still fits in 8 GB. One note on licensing: the Gemma license is open-weight rather than open-source, with Google's own use terms, so read them before shipping, though commercial use is permitted.