Can RTX 3060 12GB run Mistral Nemo 12B?
Yes — runs locally
~19 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The RTX 3060 12GB (12 GB VRAM) handles Mistral Nemo 12B comfortably using the Q4_K_M quantization, which fits in 7.5 GB. Expected throughput is around 19 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Mistral's 12B model with excellent instruction following.
Setup tutorial: Mistral Nemo 12B on RTX 3060 12GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on a NVIDIA GeForce RTX 3060 12GB with Ollama using the Q4_K_M quantization. Expect 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 or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you should expect a token generation rate of approximately 60 tok/sec, with 7.5GB of VRAM in use. This leaves about 4.5GB of VRAM for context, allowing for a practical context window of around 16K tokens.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q4_K_M quantized version of Mistral Nemo 12B, which is a 7.0GB file.
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 3060 12GB
For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 24 to balance between speed and memory usage. Enable flash attention (--flash-attn) to reduce memory consumption and improve speed. Given the 12GB VRAM, you can achieve a practical context window of around 16K tokens while maintaining ~60 tok/sec.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers 16 or lower.
Slow token generation rate
Ensure flash attention is enabled with --flash-attn and check your CUDA installation.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for low-level customization, or Jan for web-based access. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 3060 12GB.
Other models that run great on RTX 3060 12GB
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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