Run Gemma 4 31B on M5 Max

Yes — Gemma 4 31B (31B) runs at 26 tok/s on M5 Max with 128 GB RAM using Q4_K_M quantization via MLX. First token latency is 0.7s. Google's flagship 31B dense model, Arena AI #3 among open models.

Speed

tok/s

First Token

0.7

seconds

RAM Needed

128

GB minimum

Engine

MLX

Benchmark Details

The LLMCheck index estimates Gemma 4 31B on M5 Max using our published methodology: Q4_K_M quantization, memory-bandwidth scaling, and cross-referenced third-party benchmarks where available. Figures are transparent estimates — own this config? Submit a real benchmark →

Metric	Value
Tokens per second	26 tok/s
Time to first token	0.7s
Quantization	Q4_K_M
Minimum RAM	128 GB
Recommended engine	MLX
Parameters	31B
Benchmark date	2026-04

Q4_K_M 31B MLX M5 Max

Setup Guide: Run Gemma 4 31B on M5 Max

The recommended engine for Gemma 4 31B on M5 Max is MLX. Install with pip and pull the model:

pip install mlx-lm

mlx_lm.generate --model mlx-community/gemma-4-31b-q4_k_m --prompt "Hello!"

Alternatively, you can use Ollama for a simpler setup:

ollama run gemma4:31b

Performance on Other Apple Silicon Chips

Chip	Speed	First Token	Min RAM	Engine
M4 Max	18 tok/s	1.1s	48 GB	MLX
M4 Pro	14 tok/s	1.4s	24 GB	Ollama

System Requirements

To run Gemma 4 31B on M5 Max you need:

• Mac with M5 Max chip (or newer)
• 128 GB unified memory minimum
• macOS 13 Ventura or later
• ~108-128 GB free disk space for the model file
• MLX installed (see our Ollama install guide)

🛒 Get a Mac that runs Gemma 4 31B

Gemma 4 31B needs about 128 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:

MacBook Pro M4 Max (128GB) → Mac Studio M4 Max →

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