Can RTX 3090 run Gemma 3 12B?

Yes — runs locally

~42 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

24 GB

Model size

12B

Best quant

Q8_0

VRAM needed

12.2 GB

The verdict

The RTX 3090 (24 GB VRAM) handles Gemma 3 12B comfortably using the Q8_0 quantization, which fits in 12.2 GB. Expected throughput is around 42 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 3090

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 3090 with Q8_0 quantization for Grade S performance at ~73 tokens/sec.

Prerequisites

Before starting, ensure you have at least 12GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.

Expected performance

You can expect the model to run at ~73 tokens/sec with 12.2GB VRAM in use, leaving 11.8GB for context. This setup allows for a practical context window of up to 32768 tokens, ensuring smooth and efficient operation.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

Download the Q8_0 quantized version of Gemma 3 12B (11.7GB file) from Hugging Face.

ollama pull bartowski/google_gemma-3-12b-it-GGUF:google_gemma-3-12b-it-Q8_0.gguf

3. Run it

ollama run google_gemma-3-12b-it-Q8_0.gguf --n-gpu-layers 12 --flash-attn --context-length 32768

4. Optimize for RTX 3090

For optimal performance on the NVIDIA GeForce RTX 3090 with 24GB VRAM, use --n-gpu-layers 12 to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage and improve speed. With 12.2GB VRAM required for the model, you will have approximately 11.8GB of VRAM left for context, allowing for a practical context window of up to 32768 tokens.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 8 or 4 to further offload layers to the CPU and free up more VRAM.

Slow inference speed

Ensure that --flash-attn is enabled and try increasing --n-gpu-layers to 16 if your system has sufficient CPU resources.

Model fails to load

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

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface, while llama.cpp offers more control over low-level optimizations. Jan is a good choice for those looking for a lightweight runtime. However, Ollama provides a balanced approach with ease of use and performance, making it the recommended choice for this GPU.

Full Gemma 3 12B details →

Other models that run great on RTX 3090

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.

Want personalized recommendations for your exact setup? Detect my hardware →