Can RTX 4060 Ti 16GB run Gemma 3 12B?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

16 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.3 GB

The verdict

The RTX 4060 Ti 16GB (16 GB VRAM) handles Gemma 3 12B comfortably using the Q4_K_M quantization, which fits in 7.3 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. High quality 12B model. Excellent for iPad Pro and Mac.

Setup tutorial: Gemma 3 12B on RTX 4060 Ti 16GB

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance at ~81 tok/sec using the Q4_K_M quantization.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect ~81 tok/sec performance with 7.3GB VRAM in use, leaving 8.7GB of VRAM for context. This setup supports a practical context window of up to 32768 tokens, making it suitable for long-form text generation tasks.

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

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 12 to offload layers to the GPU, enable --flash-attn for efficient attention computation, and consider using tensor parallelism if you need to scale further. The 16GB VRAM allows you to run the model with a large context window while maintaining high throughput.

Troubleshooting

Out of memory errors during inference

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

Slow token generation speed

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

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or if you prefer a different interface. LM Studio offers a graphical interface, llama.cpp provides a lightweight and highly optimized runtime, and Jan is ideal for distributed training and inference. However, Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.

Full Gemma 3 12B details →

Other models that run great on RTX 4060 Ti 16GB

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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