Can RTX 3080 Ti run Mistral Nemo 12B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 3080 Ti (12 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 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an NVIDIA GeForce RTX 3080 Ti with Q4_K_M quantization for 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 10/11 or Linux), and the latest NVIDIA drivers (version 510.47.03 or later) with CUDA 11.4 or higher installed.
Expected performance
You can expect the model to run at approximately 60 tokens per second with 7.5GB VRAM in use, leaving 4.5GB for context. This setup should provide a snappy and responsive experience with a practical context window of up to 4096 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. 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
ollama chat --model Mistral-Nemo-Instruct-2407-Q4_K_M.gguf4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 7.5GB VRAM required for the model, you will have approximately 4.5GB left for context, allowing for a practical context window of around 4096 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers 24 and decrease the context window to 2048 tokens.
Slow token generation rate
Ensure flash attention is enabled with --flash-attn and check that your CUDA drivers are up to date.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp is highly customizable and can be fine-tuned for specific use cases, while Jan provides a lightweight and efficient runtime, ideal for systems with limited resources. Choose based on your specific needs and preferences.
Other models that run great on RTX 3080 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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