Gemma 4 on Mac: Which Variant Fits Your Setup?
Gemma 4 on Apple Silicon: E2B, E4B, 26B or 31B — which model for which Mac.
Gemma 4 is Google’s current open model family. For Mac users, it’s interesting because the lineup ranges from tiny E2B to 31B Dense. But the question isn’t “what can the model do?” — it’s “what fits on my Mac?”
The short answer
8 GB Mac: gemma4:e2b (7.2 GB). Runs, but barely. More of an experiment than a daily driver.
16 GB Mac: gemma4:e4b (9.6 GB) is the sweet spot. Enough memory for everyday use, vision works, and context handles most tasks.
24-32 GB Mac: gemma4:26b (18 GB) — the MoE model with only 3.8B active parameters per token. Fast, efficient, and quality is surprisingly good for the activation rate.
48+ GB: gemma4:31b (20 GB). The full package with 31B dense parameters. More quality, but significantly more memory and slower token generation.
What I tested on my Mac Mini M4
I ran all variants on my 32 GB machine. Here’s what I noticed:
gemma4:e4b has become my daily driver. It runs fast, vision works reliably, and quality is enough for most coding and chat tasks. Memory usage is manageable — on 32 GB, plenty of room for other apps.
gemma4:26b is the quality-speed compromise. The MoE design means only 3.8B of 26B parameters activate per token. That makes it faster than a dense model of similar size. Runs well on 32 GB, but KV cache for long contexts grows quickly.
gemma4:31b is the full package. On 32 GB, it gets tight for longer contexts. On 48 GB, it’s the sweet spot for maximum quality. But honestly: the difference to 26b is smaller in practice than you’d expect.
Thinking Mode — when is it worth it?
Gemma 4 supports configurable thinking mode. The model “thinks” before answering — useful for math, logic, and multi-step planning. But it extends response time and uses more context.
My tip: Disable for quick everyday questions. Enable for complex coding tasks. The difference is noticeable — but not always worth the wait.
Ollama setup
ollama pull gemma4:e4b # or 26b or 31b
ollama run gemma4:e4bFor vision, just pass an image:
ollama run gemma4:e4b "What do you see in this screenshot?"The normal Ollama tags are listed as text+image. The *-mlx tags are text-only — only for the MLX alternative.
The 256K reality
26B and 31B support up to 256K tokens context. But that’s a model limit, not a promise. KV cache grows with context, and suddenly an 18 GB model needs 40+ GB. My tip: start at 8K, increase gradually, and check with ollama ps whether GPU offload is still complete.
Gemma 4 vs Qwen3
Qwen3 30B-A3B (MoE) is often faster on similar hardware and slightly ahead for coding. Gemma 4 excels at multimodal tasks (image+text) and has native Apple silicon optimization in MLX. For German, both are solid — Gemma 4 has slight advantages in grammar. Choose Qwen3 for coding, Gemma 4 for multimodal.
My verdict
Gemma 4 is the best open model family for serious local AI on Mac. The range from E2B to 31B means there’s a variant for every Mac.
My tip: Start with gemma4:e4b. If it’s too weak, upscale to 26b. That’s the fastest way to find the right variant for your workflow.
Tested June 2026 on Mac Mini M4 with 32 GB. All info based on official Google sources and personal testing.
Transparency
Sources and review basis
These primary and reference sources form the basis of the technical assessment. Vendor claims and external benchmarks are identified as such in the article.