Can RTX 4090 run Mistral Nemo 12B?

Yes — runs locally

~66 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

12B

Best quant

Q8_0

VRAM needed

12.6 GB

The verdict

The RTX 4090 (24 GB VRAM) handles Mistral Nemo 12B comfortably using the Q8_0 quantization, which fits in 12.6 GB. Expected throughput is around 66 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Mistral's 12B model with excellent instruction following.

Setup tutorial: Mistral Nemo 12B on RTX 4090

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

TL;DR

Run Mistral Nemo 12B on an NVIDIA GeForce RTX 4090 with Ollama using the Q8_0 quantization for Grade S performance at ~71 tok/sec.

Prerequisites

Before starting, ensure you have at least 12.1GB of free disk space, a compatible OS (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 or later installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 71 tokens per second, utilizing 12.6GB of VRAM. The remaining 11.4GB of VRAM provides ample headroom for a large context window, enabling efficient handling of long sequences.

1. Install runtimeOllama

curl -fsSL https://ollama.ai/install.sh | sh
ollama install

2. Download the model

Download the Q8_0 quantized version of Mistral Nemo 12B (12.1GB file) from Hugging Face.

ollama pull bartowski/Mistral-Nemo-Instruct-2407-GGUF:Mistral-Nemo-Instruct-2407-Q8_0.gguf

3. Run it

ollama run Mistral-Nemo-Instruct-2407-Q8_0.gguf
ollama chat --model Mistral-Nemo-Instruct-2407-Q8_0.gguf

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Enable flash attention (--flash-attn) to speed up inference. With 12.6GB VRAM usage, you have 11.4GB of headroom for context, allowing for 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 or decrease the context length to fit within the available VRAM.

Slow inference speeds

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 the Ollama runtime is properly installed.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4090.

Full Mistral Nemo 12B details →

Other models that run great on RTX 4090

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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