Can M3 Max run Mistral Nemo 12B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M3 Max (128 GB VRAM) handles Mistral Nemo 12B comfortably using the Q8_0 quantization, which fits in 12.6 GB. Expected throughput is around 36 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Mistral's 12B model with excellent instruction following.
Setup tutorial: Mistral Nemo 12B on M3 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an Apple M3 Max with Grade S performance at ~161 tok/sec using the Q8_0 quantization. Requires 12.6GB VRAM, leaving ample headroom for large contexts.
Prerequisites
Before starting, ensure you have at least 12.1GB of free disk space, macOS 13.0 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 ~161 tok/sec performance, utilizing 12.6GB of the M3 Max's 128GB VRAM. This leaves 115.4GB of VRAM available for context, enabling you to handle very large inputs and maintain high throughput.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Mistral Nemo 12B, which is a 12.1GB file from the Hugging Face repository.
ollama pull bartowski/Mistral-Nemo-Instruct-2407-GGUF:Mistral-Nemo-Instruct-2407-Q8_0.gguf3. Run it
ollama run Mistral-Nemo-Instruct-2407-Q8_0.gguf
ollama chat4. Optimize for M3 Max
To optimize performance on the Apple M3 Max, use the Metal/MLX backend to leverage the GPU's 128GB VRAM. Ensure that MPS layers are enabled to take full advantage of the unified memory architecture. With 12.6GB VRAM used by the model, you will have 115.4GB of VRAM left for context, allowing for very large context windows.
Troubleshooting
Ollama fails to initialize with an error about missing dependencies.
Ensure Xcode Command Line Tools are installed by running `xcode-select --install`.
The model runs but is much slower than expected.
Check if the Metal/MLX backend is enabled and if MPS layers are properly configured. You can verify this by checking the Ollama logs with `ollama logs`.
Insufficient VRAM to load the model.
Verify that you have at least 128GB of VRAM available. If not, consider using a different quantization or a smaller model.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a more graphical interface, llama.cpp for command-line flexibility, or MLX for direct Metal integration. Jan is another option for those who prefer a web-based interface. Choose based on your specific needs and preferences, but Ollama provides the best balance of performance and ease of use on the Apple M3 Max.
Other models that run great on M3 Max
FAQ (20)
What GPU do I need to run Mistral Nemo 12B?
To run Mistral Nemo 12B, you need a GPU with at least 7.5 GB of VRAM for the lowest quantization level, up to 12.6 GB for the highest. NVIDIA RTX 3060 or better is recommended.
Is Mistral Nemo 12B good for coding?
Mistral Nemo 12B is well-suited for coding tasks due to its strong instruction-following capabilities and large context length of 131,072 tokens.
Mistral Nemo 12B vs Llama 3.1 8B?
Mistral Nemo 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs 4,096), making it generally more powerful but requiring more VRAM.
Can I run Mistral Nemo 12B on a Mac?
Yes, you can run Mistral Nemo 12B on a Mac with an M1 or M2 chip, but performance will be better on a machine with a dedicated GPU.
How much VRAM does Mistral Nemo 12B need?
The VRAM requirement for Mistral Nemo 12B ranges from 7.5 GB to 12.6 GB, depending on the quantization level used.
Is Mistral Nemo 12B censored?
Mistral Nemo 12B is not inherently censored, but it follows ethical guidelines and can be fine-tuned to avoid generating harmful content.
Is Mistral Nemo 12B commercial-use allowed?
Yes, Mistral Nemo 12B is licensed under Apache-2.0, which allows for commercial use without additional fees.
Mistral Nemo 12B context length?
Mistral Nemo 12B has a context length of 131,072 tokens, allowing it to process very long sequences of text.
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