Can RTX 3090 Ti run Mistral Nemo Base 12B?
Yes — runs locally
~42 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 42 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 3090 Ti with Q4_K_M quantization for Grade S performance at ~116 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 470.82.01 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 116 tokens per second, using 7.7GB of VRAM. This leaves 16.3GB of VRAM for context, allowing for a practical context window of up to 131072 tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q4_K_M quantized model (7.2GB file) from Hugging Face.
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 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash-attn for faster attention computation, and consider using tensor parallelism to distribute the model across multiple GPUs if available. With Q4_K_M quantization, you can achieve ~116 tok/sec while keeping VRAM usage at 7.7GB, leaving 16.3GB for context and other tasks.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using the --n-gpu-layers flag or decrease the batch size.
Slow inference speed
Ensure that flash-attn is enabled and that the CUDA toolkit is up to date. You can also try increasing the number of threads using the --threads flag.
Model fails to load
Verify that the model file has been downloaded correctly and is not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more customization options or specific features. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization and performance tuning, and Jan is suitable for distributed training and inference setups. However, for most users, Ollama provides a simple and efficient way to run the model on the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 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.
Want personalized recommendations for your exact setup? Detect my hardware →