Can RTX 4060 Ti 16GB run Mistral Nemo 12B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4060 Ti 16GB (16 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on your NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the Q4_K_M quantization for ~80 tok/sec speed.
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 should expect a token generation speed of approximately 80 tok/sec, with around 7.5GB of VRAM in use. This leaves you with 8.5GB of VRAM headroom, allowing you to maintain a large context window of up to 131072 tokens without running out of memory.
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 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between performance and memory usage. Enable flash attention with --flash-attn to reduce memory consumption and improve speed. With 16GB VRAM, you can achieve a practical context window of up to 131072 tokens while maintaining ~80 tok/sec.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 24 or 16 to decrease VRAM usage.
Slow token generation speed
Ensure that flash attention is enabled with --flash-attn and that your CUDA drivers are up to date.
Model fails to load
Verify that the model file has been downloaded correctly and that there are no issues with the file integrity.
Alternative runtimes
For users who prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, or Jan for a lightweight, easy-to-deploy solution. Each runtime has its strengths, but Ollama provides a good balance of ease of use and performance for the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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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