Can RTX 4080 SUPER run Gemma 2 9B Instruct?

Yes — runs locally

~78 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

9.2B

Best quant

Q8_0

VRAM needed

9.7 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q8_0 quantization, which fits in 9.7 GB. Expected throughput is around 78 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 4080 SUPER

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~66 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 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 66 tokens per second, using around 9.7GB of VRAM. This leaves about 6.3GB of VRAM for context, allowing for a practical context window of up to 8192 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Gemma 2 9B Instruct (9.2GB file).

ollama pull bartowski/gemma-2-9b-it-GGUF:gemma-2-9b-it-Q8_0.gguf

3. Run it

ollama run gemma-2-9b-it-Q8_0 --n-gpu-layers 16 --flash-attn --tensor-parallelism 2
ollama chat gemma-2-9b-it-Q8_0

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers 16 flag to offload layers to the GPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 2 to distribute the workload across multiple GPU cores. This configuration ensures that the model runs efficiently within the 16GB VRAM limit.

Troubleshooting

Out of memory errors during inference.

Reduce the number of GPU layers with --n-gpu-layers 8 or decrease the batch size.

Slow inference speed.

Ensure that --flash-attn is enabled and try increasing --tensor-parallelism to 4.

Model fails to load.

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model file.

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for lightweight deployment. Each runtime has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4080 SUPER.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 4080 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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