Can RTX 5080 run Mistral Nemo 12B?

Yes — runs locally

~48 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

16 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.5 GB

The verdict

The RTX 5080 (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 5080

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Mistral Nemo 12B on an NVIDIA GeForce RTX 5080 with Grade S performance, using the Q4_K_M quantization for ~80 tok/sec speed.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 80 tokens per second, consuming around 7.5GB of VRAM. The remaining 8.5GB of VRAM provides ample headroom for handling large context windows, allowing for efficient and snappy interaction.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. 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.gguf

3. Run it

ollama run Mistral-Nemo-Instruct-2407-Q4_K_M.gguf --interactive
ollama chat Mistral-Nemo-Instruct-2407-Q4_K_M.gguf

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size 1. This configuration will utilize approximately 7.5GB of VRAM, leaving 8.5GB for context, enabling a practical context window of around 100,000 tokens.

Troubleshooting

Out of memory errors during inference.

Reduce the number of GPU layers with --n-gpu-layers <num_layers> or decrease the context length with --context-length <length>.

Slow inference speed.

Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is correctly configured with ollama config set device cuda.

Model fails to load.

Verify that the model file has been downloaded correctly and that the Ollama runtime is up to date with pip install --upgrade ollama.

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 graphical interface for easier management, while llama.cpp provides low-level control and optimization options. Jan is suitable for distributed setups or when running multiple models simultaneously. For most users, Ollama provides a balanced approach with good performance and ease of use.

Full Mistral Nemo 12B details →

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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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