Can RTX 4070 Ti SUPER run Mistral Nemo Base 12B?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 Ti SUPER (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 42 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 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 4070 Ti SUPER 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, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 77 tokens per second, using about 7.7GB of VRAM. This leaves you with around 8.3GB of VRAM for context, enabling a practical context window of up to 65,000 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. 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.gguf3. Run it
ollama run Mistral-Nemo-Base-2407-Q4_K_M.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 14. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU's memory. Enable --flash-attn for faster and more efficient attention computations. Given the 7.7GB VRAM usage, you will have approximately 8.3GB of VRAM left for context, allowing for a practical context window of around 65,000 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers to 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure that --flash-attn is enabled and try increasing the --tensor-parallelism to 2 if your system supports it.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different scenarios. LM Studio offers a user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp is highly optimized for low-memory systems and can be a good choice if you need to push the limits of your GPU's VRAM. Jan is another lightweight option that can be useful for quick prototyping or testing. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended choice for this GPU.
Other models that run great on RTX 4070 Ti 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.
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