Can RTX 3070 Ti run Mistral Nemo Base 12B?

Yes — runs locally

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

Your VRAM

8 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.7 GB

The verdict

The RTX 3070 Ti (8 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 3070 Ti

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

TL;DR

The Mistral Nemo Base 12B runs on an NVIDIA GeForce RTX 3070 Ti with a Grade B performance at ~39 tok/sec using the Q4_K_M quantization. This setup is efficient and snappy.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 39 tokens per second, utilizing around 7.7GB of VRAM. This leaves about 0.3GB of VRAM for context, allowing for a practical context window of up to 1024 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

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 --model Mistral-Nemo-Base-2407-GGUF --quantization Q4_K_M
ollama chat

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers parameter to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce VRAM usage and improve speed. Given the 8GB limit, set --n-gpu-layers to 20 to balance between speed and memory usage.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers using --n-gpu-layers 10 and enable flash attention with --flash-attn.

Slow token generation

Ensure that the latest NVIDIA drivers and CUDA are installed, and try increasing the batch size with --batch-size 16.

Model fails to load

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

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lower-level control, or Jan for specialized use cases. Ollama is generally recommended for its ease of use and compatibility with the Q4_K_M quantization on the NVIDIA GeForce RTX 3070 Ti.

Full Mistral Nemo Base 12B details →

Other models that run great on RTX 3070 Ti

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 →