Can RTX 4080 SUPER run Gemma 3 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

7.3 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles Gemma 3 12B comfortably using the Q4_K_M quantization, which fits in 7.3 GB. Expected throughput is around 48 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. High quality 12B model. Excellent for iPad Pro and Mac.

Setup tutorial: Gemma 3 12B on RTX 4080 SUPER

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 4080 SUPER with grade S performance at ~81 tok/sec using the Q4_K_M quantization. Requires 7.3GB VRAM.

Prerequisites

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

Expected performance

You can expect ~81 tok/sec performance with 7.3GB VRAM in use, leaving 8.7GB for context. This setup allows for a practical context window of up to 32K tokens, making it suitable for long-form content generation.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized version of Gemma 3 12B (6.8GB file) from Hugging Face.

ollama pull bartowski/google_gemma-3-12b-it-GGUF:google_gemma-3-12b-it-Q4_K_M.gguf

3. Run it

ollama run google_gemma-3-12b-it-Q4_K_M --n-gpu-layers 12 --flash-attn --context-length 32768

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 12 to utilize most of the GPU memory while leaving some headroom. Enable --flash-attn for faster inference and better memory efficiency. With 7.3GB VRAM used, you have 8.7GB left for context, allowing for a large practical context window.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 8 or 6 to lower VRAM usage.

Slow inference speed

Ensure --flash-attn is enabled and check if your CUDA installation is up-to-date.

Model fails to load

Verify the integrity of the downloaded model file and try re-downloading it.

Alternative runtimes

Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, and Jan for advanced customization options. Use LM Studio for ease of use, llama.cpp for minimal resource consumption, and Jan for fine-tuning and custom modifications.

Full Gemma 3 12B details →

Other models that run great on RTX 4080 SUPER

FAQ (20)

What GPU do I need to run Gemma 3 12B?

To run Gemma 3 12B, you need a GPU with at least 7.3 GB of VRAM, but 12.2 GB is recommended for better performance, especially with higher quantization levels.

Is Gemma 3 12B good for coding?

Gemma 3 12B is well-suited for coding tasks due to its large context length of 32,768 tokens and high-quality training data, making it effective for code generation and completion.

Gemma 3 12B vs Llama 3.1 8B?

Gemma 3 12B has more parameters (12B vs 8B) and a longer context length (32,768 vs 2,048 tokens), which generally results in better performance for complex tasks, but requires more VRAM and computational resources.

Can I run Gemma 3 12B on a Mac?

Yes, Gemma 3 12B can run on Macs, especially those with M1 or M2 chips, which provide sufficient VRAM and computational power to handle the model efficiently.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B requires between 7.3 GB and 12.2 GB of VRAM, depending on the quantization level used. Higher quantization levels reduce VRAM usage but may slightly impact performance.

Is Gemma 3 12B censored?

Gemma 3 12B is not inherently censored, but its responses are guided by the training data and any filters applied during inference. Users can implement additional content moderation as needed.

Is Gemma 3 12B commercial-use allowed?

Yes, Gemma 3 12B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.

Gemma 3 12B context length?

Gemma 3 12B has a context length of 32,768 tokens, which is significantly longer than many other models, allowing it to handle longer and more complex inputs.

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