Can M4 Max run Mistral Nemo 12B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The M4 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 M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an Apple M4 Max with Grade S performance, using the Q8_0 quantization for ~161 tok/sec.
Prerequisites
Before starting, ensure you have at least 12.1GB 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 the model to run at approximately 161 tokens per second, utilizing 12.6GB of VRAM. Given the 128GB of total VRAM, you will have 115.4GB of headroom for context, allowing for a practical context window of up to 131,072 tokens.
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 (12.1GB file) from Hugging Face.
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 chat --model Mistral-Nemo-Instruct-2407-Q8_0.gguf4. Optimize for M4 Max
To optimize performance on the Apple M4 Max, leverage the Metal/MLX backend and utilize the 128GB of unified memory. Ensure that MPS (Metal Performance Shaders) layers are enabled to take full advantage of the GPU. The large amount of VRAM allows for efficient handling of the 12.6GB required by the Q8_0 quantization.
Troubleshooting
Low token generation speed
Ensure that the Metal/MLX backend is properly configured and that MPS layers are enabled. You can check this by running `ollama config --list` and verifying the settings.
Out of memory errors
Reduce the batch size or context length to fit within the available VRAM. You can adjust these settings in the Ollama configuration using `ollama config --set batch_size=32` and `ollama config --set context_length=65536`.
Model not loading
Verify that the model file has been downloaded correctly and is accessible. You can check the download location by running `ollama models` and ensure the file path is correct.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio, llama.cpp, or MLX. LM Studio provides a more graphical interface, while llama.cpp offers more control over low-level optimizations. MLX is another viable option for leveraging Metal Performance Shaders. Choose an alternative based on your specific needs for interface, control, or performance tuning.
Other models that run great on M4 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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