Question 1

Can I run Gemma 3n E2B on a Apple M3 Ultra?

Accepted Answer

Yes — Apple M3 Ultra runs Gemma 3n E2B fully on GPU at the FP16 quantization. Full GPU inference — 192 GB VRAM meets the 5.2 GB recommendation.

Question 2

What quantization of Gemma 3n E2B should I use on a Apple M3 Ultra?

Accepted Answer

For 192 GB VRAM on the Apple M3 Ultra, the FP16 variant is the best fit. Estimated ~200 tokens/sec on the FP16 quantization.

Question 3

How fast does Gemma 3n E2B run on Apple M3 Ultra?

Accepted Answer

Roughly 200 tokens/sec for FP16. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if Apple M3 Ultra is not enough for Gemma 3n E2B?

Accepted Answer

Try a smaller quantization variant or run on CPU + RAM with the trade-off of slower generation.

Quantization	File Size	Min VRAM	Rec VRAM	Context	Verdict	Estimated tok/s
Q4_K_M	1 GB	1.2 GB	1.3 GB	8K / 8K	Full GPU	~640
Q5_K_M	1.3 GB	1.5 GB	1.7 GB	8K / 8K	Full GPU	~535.1
Q8_0	2 GB	2.3 GB	2.6 GB	8K / 8K	Full GPU	~381
FP16Best fit	4 GB	4.6 GB	5.2 GB	8K / 8K	Full GPU	~200

Can I Run Gemma 3n E2B on Apple M3 Ultra?

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