Can RTX 4080 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 (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

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 4080 with Grade S performance, using the Q4_K_M quantization for ~81 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible OS (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect ~81 tok/sec performance, using 7.3GB of VRAM. The remaining 8.7GB of VRAM provides ample headroom for a large context window, enabling you to work with sequences of up to 24K tokens effectively.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized version of Gemma 3 12B, which is 6.8GB in size.

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 32 --flash-attn --context-length 32768

4. Optimize for RTX 4080

For optimal performance on the NVIDIA GeForce RTX 4080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention computations. With 7.3GB VRAM used by the model, you have 8.7GB of VRAM left for context, allowing a practical context window of around 24K tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or decrease the context length using --context-length.

Slow inference speed

Ensure that --flash-attn is enabled and try increasing the batch size if supported by your application.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight and portable deployment, or Jan for advanced customization options. Each runtime has its own strengths, but Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4080.

Full Gemma 3 12B details →

Other models that run great on RTX 4080

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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