Can RTX 3080 Ti run Mistral Nemo Base 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 Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Official Mistral-Nemo 12B foundation model (NVIDIA collab) — pretrained only, no instruct or refusal layer. Naturally uncensored, Apache 2.0, 128K context.
Setup tutorial: Mistral Nemo Base 12B on RTX 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 3080 Ti with Q4_K_M quantization for Grade S performance at ~58 tok/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 470.82 or later, and CUDA 11.4 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 58 tokens per second, using 7.7GB of VRAM. The remaining 4.3GB of VRAM provides sufficient headroom to handle a context window of up to 65,000 tokens, ensuring smooth and efficient inference.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized model (7.2GB file) from the Hugging Face repository.
ollama pull bartowski/Mistral-Nemo-Base-2407-GGUF:Mistral-Nemo-Base-2407-Q4_K_M.gguf3. Run it
ollama run Mistral-Nemo-Base-2407-Q4_K_M.gguf --interactive
ollama chat Mistral-Nemo-Base-2407-Q4_K_M.gguf4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to balance between speed and memory usage. Enable flash attention with --flash-attn to reduce memory consumption and improve inference speed. Given the 7.7GB VRAM requirement, you will have approximately 4.3GB of VRAM left for context, allowing for a practical context window of around 65,000 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the context window size.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify that the model file has been downloaded correctly and that there are no issues with the Ollama installation.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for different scenarios. LM Studio offers a user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp is ideal for low-memory systems and provides more control over quantization and optimization settings. Jan is a lightweight runtime that is easy to set up and use, making it a good choice for quick prototyping and testing.
Other models that run great on RTX 3080 Ti
FAQ (20)
What GPU do I need to run Mistral Nemo Base 12B?
To run Mistral Nemo Base 12B, you need a GPU with at least 7.7 GB of VRAM, but 24.5 GB is recommended for better performance, especially with higher quantization levels.
Is Mistral Nemo Base 12B good for coding?
Mistral Nemo Base 12B is a versatile model that can handle coding tasks well, thanks to its large context length of 131,072 tokens and strong language understanding capabilities.
Mistral Nemo Base 12B vs Llama 3.1 8B?
Mistral Nemo Base 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs typically 2,048 tokens), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mistral Nemo Base 12B on a Mac?
Yes, you can run Mistral Nemo Base 12B on a Mac with an NVIDIA GPU and sufficient VRAM. Ensure you have the necessary drivers and CUDA support installed.
How much VRAM does Mistral Nemo Base 12B need?
Mistral Nemo Base 12B requires between 7.7 GB and 24.5 GB of VRAM, depending on the quantization level used. Higher quantization reduces VRAM usage but may affect performance.
Is Mistral Nemo Base 12B censored?
No, Mistral Nemo Base 12B is naturally uncensored, allowing it to generate content without predefined restrictions.
Is Mistral Nemo Base 12B commercial-use allowed?
Yes, Mistral Nemo Base 12B is licensed under Apache 2.0, which allows commercial use as long as you comply with the license terms.
Mistral Nemo Base 12B context length?
Mistral Nemo Base 12B has a context length of 131,072 tokens, making it suitable for handling very long sequences of text.
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