Can M4 Pro run Gemma 3 4B?
Yes — runs locally
~62 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Pro (48 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 62 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Balanced 4B model with strong reasoning. Great for iPhones.
Setup tutorial: Gemma 3 4B on M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B on an Apple M4 Pro with a Grade S performance, using the Q8_0 quantization. Expect ~223 tok/sec and efficient VRAM usage.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, macOS 12.3 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.
Expected performance
With the Q8_0 quantization, you can expect a throughput of ~223 tok/sec, utilizing 4.3GB of VRAM. Given the 48GB VRAM, you have 43.6GB of headroom, which allows for a practical context window of up to 32768 tokens, ensuring smooth and efficient model operation.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama setup2. Download the model
Download the Q8_0 quantized version of Gemma 3 4B (3.8GB file) from Hugging Face.
ollama pull bartowski/google_gemma-3-4b-it-GGUF:google_gemma-3-4b-it-Q8_0.gguf3. Run it
ollama run google_gemma-3-4b-it-Q8_0.gguf
ollama chat --model google_gemma-3-4b-it-Q8_0.gguf4. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, use the Metal/MLX backend to leverage the 48GB of unified memory. Ensure that MPS (Metal Performance Shaders) layers are enabled to take advantage of the GPU's capabilities. With 4.3GB VRAM in use, you will have 43.6GB of headroom for context, allowing for large context windows without performance degradation.
Troubleshooting
Low throughput or high latency
Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. Check for any background processes consuming resources.
Out-of-memory errors
Reduce the context length if necessary. Use the `--context-length` flag to adjust the context window size.
Model not loading
Verify the integrity of the downloaded model file. Re-run the `ollama pull` command to ensure the model is correctly downloaded.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, alternatives like LM Studio, llama.cpp, and MLX can be used for more advanced customization or specific use cases. For example, LM Studio provides a graphical interface, while llama.cpp offers more control over quantization and optimization settings. MLX is ideal for integrating the model into custom applications.
Other models that run great on M4 Pro
FAQ (20)
What GPU do I need to run Gemma 3 4B?
To run Gemma 3 4B, you need a GPU with at least 2.8 GB of VRAM for the lowest quantization level, up to 4.3 GB for higher quantizations.
Is Gemma 3 4B good for coding?
Gemma 3 4B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 32,768 tokens.
Gemma 3 4B vs Llama 3.1 8B?
Gemma 3 4B has fewer parameters (4B vs 8B) but offers a larger context length (32,768 tokens) and better performance on mobile devices like iPhones.
Can I run Gemma 3 4B on a Mac?
Yes, you can run Gemma 3 4B on a Mac, especially if your Mac has a compatible GPU with at least 2.8 GB of VRAM.
How much VRAM does Gemma 3 4B need?
Gemma 3 4B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used.
Is Gemma 3 4B censored?
Gemma 3 4B is not inherently censored, but its responses may be filtered based on the implementation and settings used.
Is Gemma 3 4B commercial-use allowed?
Gemma 3 4B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 4B context length?
Gemma 3 4B has a context length of 32,768 tokens, allowing it to handle very long sequences of text.
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