Can RTX 3070 Ti run Mistral Nemo 12B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Mistral Nemo 12B comfortably using the Q4_K_M quantization, which fits in 7.5 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Mistral's 12B model with excellent instruction following.
Setup tutorial: Mistral Nemo 12B on RTX 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an NVIDIA GeForce RTX 3070 Ti with a Grade B performance, using the Q4_K_M quantization for ~40 tok/sec speed.
Prerequisites
Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 510.47.03 or later), and CUDA 11.4 or later installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 40 tokens per second, using around 7.5GB of VRAM. This leaves about 0.5GB of VRAM for context, allowing for a practical context window of up to 131072 tokens, though this may vary depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 12 --flash-attn --context-length 1310724. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use --n-gpu-layers 12 to offload some layers to CPU, enable --flash-attn for efficient attention computation, and set --context-length to 131072. This configuration will help maintain ~40 tok/sec while keeping VRAM usage within the 8GB limit.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 8 or lower, and decrease the context length if necessary.
Slow token generation
Ensure that --flash-attn is enabled and try increasing the batch size with --batch-size 16.
Model fails to load
Check that the model file is fully downloaded and not corrupted. Re-run the download command if needed.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, and Jan for advanced customization options. However, Ollama provides a balanced approach with good performance and ease of use, making it suitable for most scenarios on the NVIDIA GeForce RTX 3070 Ti.
Other models that run great on RTX 3070 Ti
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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