Can RTX 4070 SUPER run Mistral Nemo 12B?
Yes — runs locally
~36 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Mistral Nemo 12B comfortably using the Q4_K_M quantization, which fits in 7.5 GB. Expected throughput is around 36 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 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B with Q4_K_M quantization on an NVIDIA GeForce RTX 4070 SUPER for Grade S performance at ~60 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.12 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 60 tokens per second, using around 7.5GB of VRAM. This leaves 4.5GB of VRAM for context, allowing for a practical context window of around 8192 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the 7.0GB Q4_K_M quantized model 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.gguf --interactive
ollama chat Mistral-Nemo-Instruct-2407-Q4_K_M.gguf4. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, 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 around 8192 tokens while maintaining ~60 tok/sec.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 24 or lower.
Slow inference speed
Enable flash attention with --flash-attn and ensure your CUDA installation is up to date.
Model fails to load
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 GUI, llama.cpp for low-level control and customization, or Jan for integrated web-based interfaces. Choose these alternatives based on your specific use case, such as needing a graphical interface or more advanced customization options.
Other models that run great on RTX 4070 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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