Can RTX 4060 Ti 16GB run Mistral Nemo Base 12B?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

16 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.7 GB

The verdict

The RTX 4060 Ti 16GB (16 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU 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 RTX 4060 Ti 16GB

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

TL;DR

Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 4060 Ti 16GB with Q4_K_M quantization for Grade S performance at ~77 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.11 or later) installed. Additionally, install CUDA 11.7 or later.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 77 tokens per second, using around 7.7GB of VRAM. This leaves about 8.3GB of VRAM for context, allowing for a practical context window of up to 131072 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized version of Mistral Nemo Base 12B (7.2GB file) from Hugging Face.

ollama pull bartowski/Mistral-Nemo-Base-2407-GGUF:Mistral-Nemo-Base-2407-Q4_K_M.gguf

3. Run it

ollama run Mistral-Nemo-Base-2407-Q4_K_M --n-gpu-layers 32 --flash-attn --tensor-parallelism 1
ollama chat Mistral-Nemo-Base-2407-Q4_K_M

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 32 to utilize the full 16GB VRAM. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 to avoid overloading the GPU. This configuration ensures that the model runs efficiently within the available VRAM.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.

Slow token generation speed

Ensure --flash-attn is enabled and try increasing --tensor-parallelism to 2 if your system can handle it.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the 'ollama pull' command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface, while llama.cpp offers more control over low-level optimizations. Jan is a lightweight option for quick prototyping. For the NVIDIA GeForce RTX 4060 Ti 16GB, Ollama provides a balanced approach with good 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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