Run Gemma 4 12B on M4

Yes — Gemma 4 12B (12B) runs at 13 tok/s on M4 with 16 GB RAM using Q4_K_M quantization via MLX. A capable open-source LLM with 12B parameters.

Speed

estimated13

tok/s

First Token

—

seconds

RAM Needed

GB minimum

Engine

MLX

Benchmark Details

The LLMCheck index estimates Gemma 4 12B on M4 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	13 tok/s
Time to first token	—
Quantization	Q4_K_M
Minimum RAM	16 GB
Recommended engine	MLX
Parameters	12B
Benchmark date	2026-10

Q4_K_M 12B MLX M4

Setup Guide: Run Gemma 4 12B on M4

The recommended engine for Gemma 4 12B on M4 is MLX. Install with pip and pull the model:

pip install mlx-lm

mlx_lm.generate --model mlx-community/gemma-4-12b-q4_k_m --prompt "Hello!"

Alternatively, you can use Ollama for a simpler setup:

ollama run gemma-4-12b

Performance on Other Apple Silicon Chips

Chip	Speed	First Token	Min RAM	Engine
M5 Max	64 tok/s	—	64 GB	MLX
M4 Max	57 tok/s	—	64 GB	MLX
M4 Pro	30 tok/s	—	24 GB	MLX

System Requirements

To run Gemma 4 12B on M4 you need:

• Mac with M4 chip (or newer)
• 16 GB unified memory minimum
• macOS 13 Ventura or later
• ~13-16 GB free disk space for the model file
• MLX installed (see our Ollama install guide)

🛒 Get a Mac that runs Gemma 4 12B

Gemma 4 12B needs about 16 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:

Mac mini M6 (24GB) on Amazon → MacBook Air M4 (24GB) on Amazon →

As an Amazon Associate, LLMCheck earns from qualifying purchases. These affiliate links cost you nothing extra and help keep the LLMCheck index free.

Compare More Models

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