Can I run Gemma 4 E4B?

Gemma 4 E4B is Google's small mixture-of-experts model, and it's a good fit if you want a local assistant that also reads images. The trick with an MoE is that only about 4.5B of its 8B parameters fire per token, so it runs at the speed of a much smaller model while still needing room for the whole thing in memory. At 4-bit it lands around 4.9 GB, which fits on a 12 GB card like an RTX 3060 with plenty of headroom, and sits fine within unified memory on an 8 GB Apple Silicon Mac. Plan for the full model, not just the active slice.

In daily use it feels quick. On an RTX 3060 you can expect around 112 tokens per second at 4-bit, and a high-end card like the RTX 4090 pushes that past 300, faster than you'll ever read. On Apple Silicon an M-Max sits near 128 tok/s, and even CPU on DDR5 manages roughly 19 tok/s if you're patient. The 128K context window is real but expensive: fill it and total memory climbs to about 21.7 GB, well past what the weights alone suggest. Keep working context to a few thousand tokens on smaller hardware and you'll stay comfortable.

Against Gemma 3 4B, the lighter sibling in the family, E4B tends to give you stronger output while keeping a similar small active footprint, and both handle vision, so it's the better default unless you're truly memory-starved. Its standout trait is that MoE speed-to-quality balance: chat and image understanding at small-model latency. And the license is the easy part. Apache 2.0 means you can use it freely, including commercially and in production, with no provider-specific strings attached. That makes it one of the simpler local models to actually ship something on.