Can M3 Max run Mistral Nemo Base 12B?

Yes — runs locally

~36 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

128 GB

Model size

12B

Best quant

BF16

VRAM needed

24.5 GB

The verdict

The M3 Max (128 GB VRAM) handles Mistral Nemo Base 12B comfortably using the BF16 quantization, which fits in 24.5 GB. Expected throughput is around 36 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Official Mistral-Nemo 12B foundation model (NVIDIA collab) — pretrained only, no instruct or refusal layer. Naturally uncensored, Apache 2.0, 128K context.

Setup tutorial: Mistral Nemo Base 12B on M3 Max

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Mistral Nemo Base 12B on an Apple M3 Max with BF16 quantization for Grade S performance at ~83 tok/sec.

Prerequisites

Before starting, ensure you have at least 50GB of free disk space, macOS Ventura 13.0 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install`.

Expected performance

You can expect the model to run at approximately 83 tokens per second, using 24.5GB of VRAM. Given the remaining 103.5GB of VRAM, you can achieve a practical context window of up to 131,072 tokens, which is the maximum supported by the model.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama setup

2. Download the model

Download the BF16 quantized version of the model (24.0GB file).

ollama pull mistralai/Mistral-Nemo-Base-2407:model.safetensors

3. Run it

ollama run mistralai/Mistral-Nemo-Base-2407
ollama chat --model mistralai/Mistral-Nemo-Base-2407

4. Optimize for M3 Max

For optimal performance on the Apple M3 Max, utilize the Metal Performance Shaders (MPS) layers and the Metal framework. The 128GB of unified memory allows for efficient data transfer between CPU and GPU. Ensure that the model is loaded into the unified memory to maximize performance and minimize latency. With 24.5GB VRAM used by the model, you have 103.5GB of remaining VRAM for context and other tasks.

Troubleshooting

Model fails to load due to insufficient VRAM.

Ensure that no other applications are using significant VRAM. Close unnecessary apps and try running the model again.

Performance is significantly lower than expected.

Check if the Metal framework and MPS layers are properly enabled. Run `ollama check` to verify the setup.

Ollama installation fails.

Ensure Homebrew is up-to-date by running `brew update` and `brew upgrade`. Try installing Ollama again with `brew install ollama`.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and MLX. LM Studio is suitable for a graphical interface and easy model management. llama.cpp is ideal for low-level control and customization. MLX is another Metal-based runtime that can be used for advanced optimization. Choose an alternative based on your specific needs for control, performance, and ease of use.

Full Mistral Nemo Base 12B details →

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FAQ (20)

What GPU do I need to run Mistral Nemo Base 12B?

To run Mistral Nemo Base 12B, you need a GPU with at least 7.7 GB of VRAM, but 24.5 GB is recommended for better performance, especially with higher quantization levels.

Is Mistral Nemo Base 12B good for coding?

Mistral Nemo Base 12B is a versatile model that can handle coding tasks well, thanks to its large context length of 131,072 tokens and strong language understanding capabilities.

Mistral Nemo Base 12B vs Llama 3.1 8B?

Mistral Nemo Base 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs typically 2,048 tokens), making it more powerful for complex tasks but requiring more VRAM.

Can I run Mistral Nemo Base 12B on a Mac?

Yes, you can run Mistral Nemo Base 12B on a Mac with an NVIDIA GPU and sufficient VRAM. Ensure you have the necessary drivers and CUDA support installed.

How much VRAM does Mistral Nemo Base 12B need?

Mistral Nemo Base 12B requires between 7.7 GB and 24.5 GB of VRAM, depending on the quantization level used. Higher quantization reduces VRAM usage but may affect performance.

Is Mistral Nemo Base 12B censored?

No, Mistral Nemo Base 12B is naturally uncensored, allowing it to generate content without predefined restrictions.

Is Mistral Nemo Base 12B commercial-use allowed?

Yes, Mistral Nemo Base 12B is licensed under Apache 2.0, which allows commercial use as long as you comply with the license terms.

Mistral Nemo Base 12B context length?

Mistral Nemo Base 12B has a context length of 131,072 tokens, making it suitable for handling very long sequences of text.

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