Can RTX 5070 Ti run Mistral Nemo 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5070 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an NVIDIA GeForce RTX 5070 Ti with Q4_K_M quantization for Grade S performance at ~80 tokens/sec.
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 recommended settings, you can expect the model to run at approximately 80 tokens/sec, using around 7.5GB of VRAM. This leaves 8.5GB of VRAM for context, enabling a practical context window of up to 131072 tokens, which is ideal for long-form content generation and complex tasks.
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.gguf --n-gpu-layers 12 --flash-attn --context-length 1310724. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 12 to utilize most of the GPU's memory while leaving some headroom. Enable --flash-attn for faster and more efficient attention computation. The model will use approximately 7.5GB of VRAM, leaving 8.5GB for context, allowing for a practical context window of up to 131072 tokens.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 8 or 6 to lower VRAM usage.
Slow token generation speed
Ensure --flash-attn is enabled and check that your CUDA installation is up to date.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
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 quantization and performance, or Jan for web-based deployment. Each has its own strengths, but Ollama provides a balanced approach for easy setup and good performance on the NVIDIA GeForce RTX 5070 Ti.
Other models that run great on RTX 5070 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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