Can RTX 5080 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 5080 (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 5080

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

TL;DR

The Gemma 3 12B model runs at Grade S on the NVIDIA GeForce RTX 5080 with the Q4_K_M quantization, achieving ~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 driver (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 81 tokens per second, using 7.3GB of VRAM. The remaining 8.7GB of VRAM provides ample headroom for a full 32768-token context window, ensuring smooth and efficient operation.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized version of Gemma 3 12B (6.8GB file) 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 32 --flash-attn --context-length 32768

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster inference and better memory management. With 7.3GB VRAM used by the model, you will have 8.7GB of VRAM headroom for context, allowing for a practical context window of up to 32768 tokens.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the context length to fit within the available VRAM.

Slow inference 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 downloaded correctly and that the Ollama runtime is properly installed.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio is ideal for a graphical interface, llama.cpp offers more fine-grained control over quantization and performance settings, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 5080.

Full Gemma 3 12B details →

Other models that run great on RTX 5080

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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