Can RTX 5060 Ti 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 5060 Ti (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 5060 Ti

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

TL;DR

Run Gemma 3 12B on your NVIDIA GeForce RTX 5060 Ti with Ollama using the Q4_K_M quantization for Grade S performance at ~81 tok/sec.

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 525.60.11 or later), and CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 81 tokens per second, using around 7.3GB of VRAM. This leaves about 8.7GB of VRAM for context, allowing for a practical context window of up to 32,768 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the 6.8GB Q4_K_M quantized version of Gemma 3 12B 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.gguf --n-gpu-layers 12 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 12 to utilize most of the VRAM while leaving some headroom. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across the GPU cores effectively.

Troubleshooting

Out of memory error during inference

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

Slow token generation speed

Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 4 if your GPU supports it.

Model fails to load

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

Alternative runtimes

For users preferring a different runtime, consider LM Studio for a more user-friendly GUI, llama.cpp for lightweight and portable deployment, or Jan for advanced customization options. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 5060 Ti.

Full Gemma 3 12B details →

Other models that run great on RTX 5060 Ti

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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