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// 48d agoMODEL RELEASE
Gemma 4 runs on 16GB Macs
BatiAI’s Ollama quantization is trying to make Google’s Gemma 4 E4B practical on a 16GB Mac mini, and the community is also pointing people toward the 26B A4B MoE variant. The core tradeoff is clear: smaller quants feel easier to live with, while larger MoE models can fit but may still drag if they spill onto CPU.
// ANALYSIS
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.
// TAGS
llminferenceself-hostedopen-weightsgemma-4
DISCOVERED
48d ago
2026-04-09
PUBLISHED
48d ago
2026-04-09
RELEVANCE
8/ 10
AUTHOR
bachlac2002