Can RTX 5060 Ti run Mistral Nemo 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5060 Ti (16 GB VRAM) handles Mistral Nemo 12B comfortably using the Q4_K_M quantization, which fits in 7.5 GB. Expected throughput is around 48 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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on your NVIDIA GeForce RTX 5060 Ti with Grade S performance at ~80 tok/sec using the Q4_K_M quantization, which requires 7.5GB VRAM.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 80 tokens per second, using around 7.5GB of VRAM. This leaves 8.5GB of VRAM for context, allowing you to maintain a large practical context window without running out of memory.
1. Install runtimeOllama
curl -fsSL https://ollama.ai/install.sh | sh
ollama install2. 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 --n-gpu-layers 12 --flash-attn
ollama chat Mistral-Nemo-Instruct-2407-Q4_K_M.gguf4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 12 to utilize most of the available VRAM while keeping some headroom. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 16GB VRAM, you can comfortably run the model with a context window of up to 131072 tokens, leaving about 8.5GB of VRAM for context.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers or decrease the context window size.
Slow inference speed
Ensure that flash attention is enabled (--flash-attn) and that your NVIDIA drivers and CUDA are up to date.
Model fails to load
Verify that the model file has been downloaded correctly and that there is sufficient disk space and VRAM available.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features not supported by Ollama. For example, LM Studio offers a graphical interface and advanced tuning options, while llama.cpp provides a lightweight, highly customizable solution. Jan is suitable for users who prefer a web-based interface or need to run models on a remote server.
Other models that run great on RTX 5060 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.
Want personalized recommendations for your exact setup? Detect my hardware →