Gemma 4 runs on 16GB Macs

The interesting part here is not just that Gemma 4 can run locally, but that MoE changes the meaning of “too big” on Apple Silicon. It can fit in memory and still be usable, but that does not guarantee a fluid interactive experience.

• –BatiAI’s `gemma4-e4b:q4` is explicitly positioned for 16GB Macs, with 128K context and tool-calling support.
• –Gemma 4 26B A4B is a MoE model with only a few billion active params per token, which is why people are calling it viable on 16GB despite the headline size.
• –For day-to-day local chat or coding, the safer recommendation is still the smaller E4B class unless the user is willing to trade latency for capability.
• –The Reddit replies reflect the usual local-LLM rule on base 16GB machines: once you start depending on CPU offload, the experience gets much less pleasant.
• –This is useful deployment guidance, but the speed claims are anecdotal and should be benchmarked against the user’s actual workload.