Can RTX 3060 12GB run Mistral Nemo Base 12B?

Yes — runs locally

~19 tok/sec · Good — slight pause, then text streams smoothly.

Your VRAM

12 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.7 GB

The verdict

The RTX 3060 12GB (12 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 19 tokens/second, which feels Good — slight pause, then text streams smoothly. 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 3060 12GB

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

TL;DR

The Mistral Nemo Base 12B model runs at Grade S on an NVIDIA GeForce RTX 3060 12GB with Q4_K_M quantization, achieving ~58 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82 or later, and CUDA 11.4 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 58 tokens per second, using around 7.7GB of VRAM. This leaves about 4.3GB of VRAM for context, allowing for a practical context window of up to 16K tokens.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q4_K_M quantized version of the model, which is 7.2GB in size.

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.gguf --interactive
ollama chat Mistral-Nemo-Base-2407-Q4_K_M.gguf

4. Optimize for RTX 3060 12GB

For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention with --flash-attn to reduce memory consumption and improve inference speed. Given the 12GB VRAM, you can achieve a practical context window of up to 16K tokens while maintaining performance.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 16 or lower.

Slow inference speed

Ensure that flash attention is enabled with --flash-attn. If still slow, try reducing the batch size with --batch-size 1.

Model fails to load

Check if the model file is corrupted or incomplete. Re-download the model using the 'ollama pull' command.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight alternative. Ollama is recommended for its ease of use and efficient CUDA backend, especially on the NVIDIA GeForce RTX 3060 12GB.

Full Mistral Nemo Base 12B details →

Other models that run great on RTX 3060 12GB

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.

Want personalized recommendations for your exact setup? Detect my hardware →