Gemma 4 on Mac: M1, M2, M3, M4 Performance Tested

Apple Silicon Macs are genuinely one of the best platforms for running local AI models. The unified memory architecture means the GPU and CPU share the same pool of RAM — so a Mac with 32GB of memory can load models that would need a dedicated 32GB GPU on a PC.

I tested Gemma 4 across the entire Apple Silicon lineup. Here's exactly what you can expect.

Why Macs Are Great for Local AI

Three things make Apple Silicon special for this:

1. Unified memory: No copying data between CPU and GPU memory. A 24GB Mac has 24GB available to the model — period.
2. Metal acceleration: Ollama and llama.cpp automatically use Metal for GPU acceleration. No setup needed.
3. Memory bandwidth: Apple's memory bandwidth is excellent relative to the price, and that's the bottleneck for LLM inference.

No NVIDIA drivers, no CUDA installation, no fumbling with Docker GPU passthrough. Install Ollama, run ollama run gemma4, and Metal acceleration is already working.

Performance by Chip

Here's what I measured with Ollama, using a 512-token prompt and 256-token generation:

M1 (2020)

The M1 base with 8GB is tight. You can run E2B, but don't expect to multitask much while the model is loaded. The M1 Pro and Max are much better — more GPU cores and higher memory bandwidth make a real difference.

M2 (2022)

The M2 Pro at 16GB is the sweet spot for most people. You can run the 26B MoE model comfortably. Remember, the 26B model only uses ~3.8B active parameters per token — see our architecture guide for why.

M3 (2023)

The M3 Max with 36GB is a fantastic AI machine. You can run the full 31B model with Q4 quantization and still have headroom for other apps. The 48GB variant lets you use higher quality Q5 quantization.

M4 (2024-2025)

The M4 generation brings noticeable speed improvements. The M4 Max with 64GB is the dream setup — run the highest quality Gemma 4 model at speeds that feel interactive.

Model Recommendations by RAM

Quick reference if you just want to know what to run:

For more details on choosing between models, check our model selection guide.

Mac Mini as an Always-On AI Server

Here's something a lot of people are doing: using a Mac Mini as a dedicated AI server. It's brilliant because:

• Low power: M4 Mac Mini idles at ~5W, runs AI inference at ~30-40W
• Silent: No fans at low-to-medium loads
• Small: Fits anywhere
• Affordable: Mac Mini M4 with 24GB starts at $799

Setup:

# Install Ollama
brew install ollama

# Start Ollama as a service (runs on boot)
brew services start ollama

# Pull your model
ollama pull gemma4:26b

# Ollama now serves on port 11434
# Access from any device on your network:
# http://mac-mini-ip:11434

To access from other devices on your network, set the host:

# In your shell profile (~/.zshrc)
export OLLAMA_HOST=0.0.0.0

# Restart Ollama
brew services restart ollama

Now any device on your LAN can use your Mac Mini AI server — your phone, tablet, other computers. Put a web UI like Open WebUI in front of it and you have a private ChatGPT alternative for your whole household.

Optimization Tips for Mac

1. Close memory-hungry apps before running large models

Safari, Chrome, and Xcode can eat gigabytes of RAM. If you're tight on memory, quit them before loading a model.

# Check available memory
memory_pressure

2. Use the right quantization

Don't default to Q8 if Q4_K_M gets you 95% of the quality at half the memory. For most tasks, Q4_K_M is the sweet spot.

3. Reduce context length for faster responses

# Default context is usually 4096-8192
# If you don't need long context:
ollama run gemma4:26b --num-ctx 2048

4. Monitor GPU utilization

# Watch Metal GPU usage
sudo powermetrics --samplers gpu_power -i 1000

5. Keep Ollama updated

Metal acceleration improvements ship regularly. Update with brew upgrade ollama.

6. Consider using LM Studio if you prefer a GUI

LM Studio gives you a clean visual interface, adjustable settings, and works great on Mac.

What About Mac vs. PC for Gemma 4?

The comparison is nuanced:

For most individual users, Mac is the easier and more pleasant experience. For production servers, NVIDIA GPUs running in Docker with vLLM give better throughput per dollar.

Next Steps

• Install and run: Ollama quickstart guide
• Pick the right model: model selection guide
• Check full hardware specs: hardware requirements
• Try the GUI approach: LM Studio guide