MLX is Apple's machine learning framework purpose-built for Apple Silicon (M1, M2, M3, M4). It leverages the unified memory architecture of Apple chips to deliver exceptional inference performance — often outperforming GPU-based setups for models that fit in memory.
Gemma 4 works excellently with MLX, making any Mac with Apple Silicon a capable AI workstation. This guide covers installation, running all Gemma 4 variants, and optimizing performance on your Mac.
Apple Silicon's unified memory architecture means no GPU VRAM limit — the entire system memory is available. A Mac with 64GB RAM can load and run models that would require a $1,500+ GPU on PC.
MLX is built by Apple specifically for Apple Silicon, using Metal compute shaders and optimized memory access patterns. It consistently delivers better tokens-per-second than generic CPU inference.
Install with pip, download a model, and start generating. No CUDA drivers, no Docker containers, no complex environment setup required.
Apple Silicon's efficiency means you can run Gemma 4 for hours on battery. Ideal for developers who want local AI without being tethered to a wall outlet.
Install mlx-lm, Apple's model serving library for MLX:
pip install mlx-lm
# Verify installation
python -c "import mlx_lm; print('MLX-LM ready')Which Gemma 4 models run well on which Macs:
E4B at INT4 quantization fits comfortably. Leave room for OS and apps.
26B MoE at INT4 (~16GB) fits but leaves little headroom. E4B is the sweet spot.
Comfortable for 26B at INT8. 31B at INT4 fits with room to spare.
Can run 31B at FP16. The ultimate Gemma 4 workstation.
mlx_lm.generate \
--model mlx-community/gemma-4-e4b-it-4bit \
--prompt "Explain quantum computing in simple terms" \
--max-tokens 512mlx_lm.chat --model mlx-community/gemma-4-e4b-it-4bitmlx_lm.server \
--model mlx-community/gemma-4-e4b-it-4bit \
--port 8080
# Then use the OpenAI-compatible API:
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "gemma-4-e4b", "messages": [{"role": "user", "content": "Hello!"}]}'mlx_lm.convert \
--hf-path google/gemma-4-e4b-it \
--mlx-path ./gemma-4-e4b-4bit \
-q --q-bits 4Safari, Chrome, and Docker can consume significant RAM. Close them before running larger models to maximize available memory for MLX.
Always use INT4 or INT8 quantized models on machines with ≤32GB RAM. The quality difference is minimal but the memory savings are substantial.
Longer context windows consume more memory. If you're running low on RAM, reduce the max context length to free up memory for the model weights.
Use Activity Monitor to watch memory pressure. If it turns yellow/red, the system is swapping to disk and inference will slow dramatically. Consider a smaller model or more quantization.
MLX requires Apple Silicon (M1 or later). For Intel Macs, use Ollama or llama.cpp instead, which support CPU inference on any Mac.
Performance varies by model and hardware: E4B on M3 Pro achieves ~30-40 tokens/second. 26B MoE on M3 Max gets ~15-20 tok/s. 31B on M2 Ultra delivers ~10-15 tok/s. These speeds are excellent for interactive use.
Both are excellent on Mac. Ollama is simpler (one-command setup) and includes a built-in API server. MLX offers more control, better memory efficiency, and often slightly faster inference. For most users, start with Ollama; switch to MLX for maximum performance.
MLX-format models are available on Hugging Face, often uploaded by the mlx-community organization. You can also convert any SafeTensors model to MLX format using mlx-lm's conversion tools.
Yes. mlx-lm supports LoRA fine-tuning on Apple Silicon. This lets you customize Gemma 4 for your domain directly on your Mac without needing a separate GPU server.
Minimum 8GB for E4B at INT4. 16GB for comfortable E4B/26B INT4 use. 36-48GB for 31B at INT4. 64GB+ for 31B at FP16. Remember that macOS itself uses 3-5GB, so plan accordingly.
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Your Mac is ready for AI. Install MLX, download Gemma 4, and start generating.