Can RTX 4070 SUPER run Mistral Nemo Base 12B?

Yes — runs locally

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

Your VRAM

12 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.7 GB

The verdict

The RTX 4070 SUPER (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 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 RTX 4070 SUPER

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

TL;DR

The Mistral Nemo Base 12B runs with Grade S performance on the NVIDIA GeForce RTX 4070 SUPER using the Q4_K_M quantization, achieving ~58 tokens per second.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows 10/11 or Linux, NVIDIA driver version 525.60.13 or later, and CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 58 tokens per second, utilizing about 7.7GB of VRAM. Given the remaining 4.3GB of VRAM, you can achieve a practical context window of up to 65,536 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 model (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.gguf --interactive
ollama chat Mistral-Nemo-Base-2407-Q4_K_M.gguf

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size 1. This configuration will help maintain the ~58 tok/sec throughput while keeping VRAM usage around 7.7GB, leaving 4.3GB for context.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers <num_layers> or decrease the batch size.

Slow inference speed

Enable flash attention with --flash-attn and ensure your CUDA installation is up to date.

Model fails to load

Verify the integrity of the downloaded model file and ensure Ollama is properly installed and initialized.

Alternative runtimes

Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for lower-level control, and Jan for web-based deployment. Use LM Studio if you prefer a graphical interface, llama.cpp for fine-grained optimization, and Jan for deploying the model in a web application. However, Ollama provides a good balance of ease of use and performance for most users on the NVIDIA GeForce RTX 4070 SUPER.

Full Mistral Nemo Base 12B details →

Other models that run great on RTX 4070 SUPER

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 →