Can RTX 4070 SUPER run Gemma 3 12B?

Yes — runs locally

~36 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.3 GB

The verdict

The RTX 4070 SUPER (12 GB VRAM) handles Gemma 3 12B comfortably using the Q4_K_M quantization, which fits in 7.3 GB. Expected throughput is around 36 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 4070 SUPER

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 4070 SUPER with Q4_K_M quantization for Grade S performance at ~61 tok/sec. 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 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 61 tokens per second, using around 7.3GB of VRAM. This leaves about 4.7GB of VRAM available for context, allowing for a practical context window of up to 16,384 tokens.

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 --tensor-parallelism 1

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use --n-gpu-layers 32 to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage, and set --tensor-parallelism 1 to avoid overloading the GPU. This configuration ensures that the model runs efficiently within the 12GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers by increasing --n-gpu-layers to 48 or 64.

Slow inference speed

Ensure that flash attention is enabled (--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 there are no issues with the Ollama installation.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the runtime environment or specific features not supported by Ollama. LM Studio is ideal for a user-friendly GUI experience, while llama.cpp offers advanced customization options. Jan is suitable for lightweight, low-resource environments.

Full Gemma 3 12B details →

Other models that run great on RTX 4070 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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