Can RTX 4080 SUPER run Mistral Nemo 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo 12B on an NVIDIA GeForce RTX 4080 SUPER with Q4_K_M quantization for Grade S performance at ~80 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows 10/11 or Linux), NVIDIA driver version 525.60.13 or later, and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect ~80 tok/sec performance, using approximately 7.5GB of VRAM. The remaining 8.5GB of VRAM provides ample headroom for a large context window, enabling efficient handling of long sequences.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized model (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 32 --flash-attn
ollama chat --model Mistral-Nemo-Instruct-2407-Q4_K_M.gguf4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention calculations. With 7.5GB VRAM used by the model, you have 8.5GB of VRAM left for context, allowing for a practical context window of around 131072 tokens.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or lower to decrease VRAM usage.
Slow token generation
Ensure that --flash-attn is enabled to optimize attention calculations.
Model not loading
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model file.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a user-friendly interface for model management, while llama.cpp provides fine-grained control over inference parameters. Jan is suitable for distributed inference across multiple GPUs. However, Ollama remains the most straightforward choice for quick and efficient deployment on the NVIDIA GeForce RTX 4080 SUPER.
Other models that run great on RTX 4080 SUPER
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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