Question 1

Can I run Gemma 4 31B on a Apple M1 Pro (10-core GPU)?

Accepted Answer

Yes — Apple M1 Pro (10-core GPU) runs Gemma 4 31B fully on GPU at the Q4_K_M quantization. Full GPU inference — 32 GB VRAM meets the 24 GB recommendation.

Question 2

What quantization of Gemma 4 31B should I use on a Apple M1 Pro (10-core GPU)?

Accepted Answer

For 32 GB VRAM on the Apple M1 Pro (10-core GPU), the Q4_K_M variant is the best fit. Estimated ~8.7 tokens/sec on the Q4_K_M quantization.

Question 3

How fast does Gemma 4 31B run on Apple M1 Pro (10-core GPU)?

Accepted Answer

Roughly 8.7 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if Apple M1 Pro (10-core GPU) is not enough for Gemma 4 31B?

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
Q3_K_M	14.5 GB	16.5 GB	20 GB	8K / 256K	Full GPU	~9.5
Q4_K_MBest fit	18.4 GB	20.5 GB	24 GB	8K / 256K	Full GPU	~8.7
Q8_0	33.2 GB	35 GB	40 GB	8K / 256K	Hybrid CPU+GPU	~2

Can I Run Gemma 4 31B on Apple M1 Pro (10-core GPU)?

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Every Gemma 4 31B quantization on Apple M1 Pro (10-core GPU)