Can RTX 4070 Ti SUPER run Mistral Nemo 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 12B comfortably using the Q4_K_M quantization, which fits in 7.5 GB. Expected throughput is around 42 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Mistral's 12B model with excellent instruction following.
Setup tutorial: Mistral Nemo 12B on RTX 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an NVIDIA GeForce RTX 4070 Ti SUPER with Ollama using the Q4_K_M quantization. Expect Grade S performance at ~80 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 525.60.13 or later) installed along with CUDA 11.8 or later.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 80 tokens per second, utilizing 7.5GB of VRAM. The remaining 8.5GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context.
1. Install runtimeOllama
curl -L https://ollama.com/install.sh | bash
ollama setup2. Download the model
Download the Q4_K_M quantized version of Mistral Nemo 12B (7.0GB file) from Hugging Face.
ollama pull bartowski/Mistral-Nemo-Instruct-2407-GGUF:Mistral-Nemo-Instruct-2407-Q4_K_M.gguf3. Run it
ollama run Mistral-Nemo-Instruct-2407-Q4_K_M --n-gpu-layers 32 --flash-attn
ollama chat Mistral-Nemo-Instruct-2407-Q4_K_M4. 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 memory while leaving some headroom. Enable --flash-attn to speed up attention computations. With 7.5GB VRAM used by the model, you have approximately 8.5GB left for context, allowing for a practical context window of around 131072 tokens.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and check that your CUDA installation is correct.
Model not loading
Verify the model file integrity and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level control, or Jan for web-based deployment. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4070 Ti SUPER.
Other models that run great on RTX 4070 Ti SUPER
FAQ (20)
What GPU do I need to run Mistral Nemo 12B?
To run Mistral Nemo 12B, you need a GPU with at least 7.5 GB of VRAM for the lowest quantization level, up to 12.6 GB for the highest. NVIDIA RTX 3060 or better is recommended.
Is Mistral Nemo 12B good for coding?
Mistral Nemo 12B is well-suited for coding tasks due to its strong instruction-following capabilities and large context length of 131,072 tokens.
Mistral Nemo 12B vs Llama 3.1 8B?
Mistral Nemo 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs 4,096), making it generally more powerful but requiring more VRAM.
Can I run Mistral Nemo 12B on a Mac?
Yes, you can run Mistral Nemo 12B on a Mac with an M1 or M2 chip, but performance will be better on a machine with a dedicated GPU.
How much VRAM does Mistral Nemo 12B need?
The VRAM requirement for Mistral Nemo 12B ranges from 7.5 GB to 12.6 GB, depending on the quantization level used.
Is Mistral Nemo 12B censored?
Mistral Nemo 12B is not inherently censored, but it follows ethical guidelines and can be fine-tuned to avoid generating harmful content.
Is Mistral Nemo 12B commercial-use allowed?
Yes, Mistral Nemo 12B is licensed under Apache-2.0, which allows for commercial use without additional fees.
Mistral Nemo 12B context length?
Mistral Nemo 12B has a context length of 131,072 tokens, allowing it to process very long sequences of text.
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