Can RTX 5060 run Gemma 3 12B?

Yes — runs locally

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

Your VRAM

8 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.3 GB

The verdict

The RTX 5060 (8 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 5060

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

TL;DR

Run Gemma 3 12B with Q4_K_M quantization on an NVIDIA GeForce RTX 5060 for a Grade B experience at ~41 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB 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

With the recommended settings, you can expect the model to run at approximately 41 tokens per second, using about 7.3GB of VRAM. This leaves around 0.7GB of VRAM for context, allowing for a practical context window of up to 10,000 tokens, depending on the complexity of the input.

1. Install runtimeOllama

curl -fsSL https://ollama.com/install.sh | sh
ollama config set runtime cuda

2. Download the model

Download the Q4_K_M quantized model (6.8GB) from the Hugging Face repository.

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 12 --flash-attn
ollama chat google_gemma-3-12b-it-Q4_K_M

4. Optimize for RTX 5060

For optimal performance on the NVIDIA GeForce RTX 5060 with 8GB VRAM, use the --n-gpu-layers 12 flag to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With these settings, you should achieve ~41 tok/sec while keeping VRAM usage around 7.3GB, leaving 0.7GB for context.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers by increasing the --n-gpu-layers value, e.g., --n-gpu-layers 16.

Slow inference speed

Ensure that flash attention is enabled with --flash-attn. If still slow, try reducing the batch size or using a lower quantization level.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or if you encounter issues with Ollama. LM Studio is ideal for GUI-based workflows, llama.cpp offers more control over quantization and optimization, and Jan is suitable for distributed computing setups.

Full Gemma 3 12B details →

Other models that run great on RTX 5060

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 →