Can RTX 4070 SUPER run Gemma 2 9B Instruct?

Yes — runs locally

~62 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

9.2B

Best quant

Q5_K_M

VRAM needed

6.7 GB

The verdict

The RTX 4070 SUPER (12 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q5_K_M quantization, which fits in 6.7 GB. Expected throughput is around 62 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Google's efficient 9B model. Great performance-to-size ratio.

Setup tutorial: Gemma 2 9B Instruct on RTX 4070 SUPER

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 4070 SUPER with Q5_K_M quantization for Grade S performance at ~71 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 or later installed.

Expected performance

With the Q5_K_M quantization, you can expect the model to run at approximately 71 tokens per second, using 6.7GB of VRAM. This leaves about 5.3GB of VRAM available for context, enabling a practical context window of around 4096 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_K_M quantized version of Gemma 2 9B Instruct (6.2GB file size) from Hugging Face.

ollama pull bartowski/gemma-2-9b-it-GGUF:Q5_K_M

3. Run it

ollama run --model gemma-2-9b-it-GGUF --quant Q5_K_M --n-gpu-layers 48 --flash-attn
ollama chat --model gemma-2-9b-it-GGUF --quant Q5_K_M

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 48 to utilize most of the GPU memory while leaving some headroom for context. Enable --flash-attn for faster and more efficient attention computations. With 6.7GB VRAM used by the model, you will have approximately 5.3GB of VRAM left for context, allowing for a practical context window of around 4096 tokens.

Troubleshooting

Out of memory errors during inference.

Reduce the number of --n-gpu-layers to 32 or 24 to lower VRAM usage.

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 has been 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 running Gemma 2 9B Instruct. LM Studio offers a graphical interface and is suitable for users who prefer a GUI. llama.cpp is highly customizable and can be fine-tuned for specific hardware configurations. Jan is lightweight and ideal for systems with limited resources. Choose based on your specific needs and preferences.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 4070 SUPER

FAQ (20)

What GPU do I need to run Gemma 2 9B Instruct?

To run Gemma 2 9B Instruct, you need a GPU with at least 5.9 GB of VRAM, but 9.7 GB is recommended for optimal performance, especially with higher precision models.

Is Gemma 2 9B Instruct good for coding?

Gemma 2 9B Instruct is well-suited for coding tasks due to its large context length of 8192 tokens, which allows it to understand and generate complex code snippets effectively.

Gemma 2 9B Instruct vs Llama 3.1 8B?

Gemma 2 9B Instruct has a slightly larger model size (9.2B parameters) and a longer context length (8192 tokens) compared to Llama 3.1 8B, potentially offering better performance in tasks requiring deeper context understanding.

Can I run Gemma 2 9B Instruct on a Mac?

Yes, you can run Gemma 2 9B Instruct on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (at least 5.9 GB).

How much VRAM does Gemma 2 9B Instruct need?

Gemma 2 9B Instruct requires between 5.9 GB and 9.7 GB of VRAM, depending on the quantization level used.

Is Gemma 2 9B Instruct censored?

Gemma 2 9B Instruct is not inherently censored, but its behavior can be controlled through the use of filters and safety mechanisms during deployment.

Is Gemma 2 9B Instruct commercial-use allowed?

Gemma 2 9B Instruct is licensed under the 'gemma' license, which generally allows for commercial use, but you should review the specific terms of the license for any restrictions.

Gemma 2 9B Instruct context length?

Gemma 2 9B Instruct has a context length of 8192 tokens, allowing it to handle long sequences of text effectively.

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