Run Gemma 4 with MLX on Apple Silicon

MLX is Apple's machine learning framework optimized for M-series chips. Running Gemma 4 with MLX uses unified memory efficiently — the GPU and CPU share the same RAM pool, so a 16 GB M3 MacBook Pro can run Gemma 4 E4B at near-GPU speeds without any discrete graphics card.

Prerequisites

• Apple Silicon Mac (M1, M2, M3, or M4)
• macOS 14.0+ (Sonoma or later)
• Python 3.10+

Install MLX LM

pip install mlx-lm

No additional CUDA or Metal configuration is required. MLX automatically targets the Apple Silicon GPU on any supported Mac.

Run Gemma 4 E4B with MLX

mlx_lm.generate \
  --model mlx-community/gemma-4-4b-it-4bit \
  --prompt "What are the key differences between Gemma 4 E4B and Gemma 4 31B?"

mlx-community maintains quantized MLX versions of all Gemma 4 models on Hugging Face. The 4bit variants are the recommended starting point for most M-series Macs.

Available Gemma 4 MLX Models

Models are downloaded automatically on first run and cached in ~/.cache/huggingface/hub.

Use Gemma 4 as a Chat Server with MLX

mlx_lm.server \
  --model mlx-community/gemma-4-4b-it-4bit \
  --port 8080

Query Gemma 4 through the OpenAI-compatible endpoint:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mlx-community/gemma-4-4b-it-4bit",
    "messages": [{"role": "user", "content": "Explain Gemma 4 thinking mode"}]
  }'

Python Inference with MLX

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/gemma-4-4b-it-4bit")
response = generate(
    model,
    tokenizer,
    prompt="Describe Gemma 4 E4B architecture",
    max_tokens=256,
    verbose=True,
)

This is useful for scripting batch Gemma 4 inference or integrating the model into a Python pipeline.

MLX vs Ollama for Gemma 4 on Apple Silicon

MLX is generally 10–20% faster than Ollama for Gemma 4 inference on Apple Silicon because it bypasses the GGUF quantization overhead and talks directly to the Metal runtime. Ollama is easier to set up and provides a simpler interactive chat experience, but for throughput-sensitive workloads MLX is the better choice for running Gemma 4 on a Mac.

Troubleshooting

"No module named 'mlx_vlm.models.gemma4'": See the fix guide. This usually means your mlx-lm version predates Gemma 4 support — run pip install --upgrade mlx-lm to resolve it.

Out of memory: Switch from 8bit to 4bit quantization. For Gemma 4 26B, ensure you have at least 24 GB of unified memory.

Slow first-run performance: MLX compiles Metal kernels on the first inference pass. Subsequent Gemma 4 calls will be significantly faster once the cache is warm.

Related

• Run Gemma 4 with Ollama — cross-platform alternative
• Gemma 4 E4B model overview
• MLX error fix