Can RTX 3080 Ti run Gemma 3 12B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 3080 Ti (12 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 3080 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 12B on an NVIDIA GeForce RTX 3080 Ti with a Grade S performance at ~61 tok/sec using the Q4_K_M quantization. The model fits within 12GB VRAM.
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) with CUDA 11.7 installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 61 tokens per second, using around 7.3GB of VRAM. This leaves 4.7GB of VRAM for context, enabling a practical context window of up to 32768 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. 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.gguf3. Run it
ollama run google_gemma-3-12b-it-Q4_K_M --n-gpu-layers 32 --flash-attn
ollama chat google_gemma-3-12b-it-Q4_K_M4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 32 to utilize the available VRAM efficiently. Enable --flash-attn to reduce memory usage and improve speed. With these settings, the model should fit comfortably within the 12GB VRAM, leaving about 4.7GB 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 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and update your NVIDIA drivers to the latest version.
Model does not load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance tuning, or Jan for lightweight deployment. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 3080 Ti.
Other models that run great on RTX 3080 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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