Question 1

Can I run Gemma 3n E2B on a Apple M2?

Accepted Answer

Yes — Apple M2 runs Gemma 3n E2B fully on GPU at the FP16 quantization. Full GPU inference — 24 GB VRAM meets the 5.2 GB recommendation.

Question 2

What quantization of Gemma 3n E2B should I use on a Apple M2?

Accepted Answer

For 24 GB VRAM on the Apple M2, the FP16 variant is the best fit. Estimated ~25 tokens/sec on the FP16 quantization.

Question 3

How fast does Gemma 3n E2B run on Apple M2?

Accepted Answer

Roughly 25 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 M2 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	~80
Q5_K_M	1.3 GB	1.5 GB	1.7 GB	8K / 8K	Full GPU	~66.9
Q8_0	2 GB	2.3 GB	2.6 GB	8K / 8K	Full GPU	~47.6
FP16Best fit	4 GB	4.6 GB	5.2 GB	8K / 8K	Full GPU	~25

Can I Run Gemma 3n E2B on Apple M2?

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