Can RTX 4090 run Gemma 3 12B?

Yes — runs locally

~66 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

12B

Best quant

Q8_0

VRAM needed

12.2 GB

The verdict

The RTX 4090 (24 GB VRAM) handles Gemma 3 12B comfortably using the Q8_0 quantization, which fits in 12.2 GB. Expected throughput is around 66 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality 12B model. Excellent for iPad Pro and Mac.

Setup tutorial: Gemma 3 12B on RTX 4090

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

TL;DR

Run Gemma 3 12B on an NVIDIA GeForce RTX 4090 with Ollama using the Q8_0 quantization for Grade S performance at ~73 tok/sec.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a compatible OS (Windows 10/11 or Linux), the latest NVIDIA driver (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 73 tokens per second, using around 12.2GB of VRAM. This leaves about 11.8GB of VRAM available for context, allowing for a practical context window of up to 32,768 tokens.

1. Install runtimeOllama

pip install ollama
ollama config set cuda=True

2. Download the model

Download the Q8_0 quantized version of Gemma 3 12B (11.7GB file) from Hugging Face.

ollama pull bartowski/google_gemma-3-12b-it-GGUF:google_gemma-3-12b-it-Q8_0.gguf

3. Run it

ollama run google_gemma-3-12b-it-Q8_0.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2
ollama chat

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use --n-gpu-layers 32 to offload some layers to the CPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 2 to utilize both GPUs if running in a multi-GPU setup. This configuration ensures that the model runs efficiently within the 24GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 16 or 8 to further offload layers to the CPU.

Slow token generation speed

Ensure CUDA is properly configured and try increasing --tensor-parallelism to 2 if running on a single GPU.

Inconsistent performance

Check for background processes consuming GPU resources and close them to ensure the GPU is dedicated to the model.

Alternative runtimes

Alternatively, you can use LM Studio or llama.cpp for more advanced customization options. LM Studio is ideal for users who prefer a graphical interface, while llama.cpp offers fine-grained control over model parameters and is suitable for command-line enthusiasts. Jan is another runtime that supports a wide range of models but may require additional setup steps.

Full Gemma 3 12B details →

Other models that run great on RTX 4090

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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