Can RTX 3090 Ti run Gemma 2 9B Instruct?

Yes — runs locally

~60 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

9.2B

Best quant

Q8_0

VRAM needed

9.7 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q8_0 quantization, which fits in 9.7 GB. Expected throughput is around 60 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 3090 Ti

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 3090 Ti with a Grade S performance, using the Q8_0 quantization, achieving ~98 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.7 or later installed.

Expected performance

With the Q8_0 quantization, you can expect ~98 tok/sec performance, utilizing 9.7GB of VRAM. The remaining 14.3GB of VRAM provides ample headroom for maintaining a large context window, enabling efficient long-form generation.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q8_0 quantized version of Gemma 2 9B Instruct, which is a 9.2GB file.

ollama pull bartowski/gemma-2-9b-it-GGUF:q8_0

3. Run it

ollama run gemma-2-9b-it-GGUF:q8_0 --n-gpu-layers 768 --flash-attn --context-length 8192

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers 768 option to offload most layers to the GPU. Enable --flash-attn for faster attention computation. The 9.7GB VRAM usage leaves 14.3GB of VRAM for context, allowing for a practical context window close to the maximum 8192 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers using --n-gpu-layers 512 or lower, or decrease the context length using --context-length 4096.

Slow token generation speed

Ensure that --flash-attn is enabled and that your CUDA drivers are up to date. Consider increasing the batch size if applicable.

Model fails to load

Verify that the model file was downloaded correctly and that there is sufficient disk space. Re-run the download command if necessary.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level control, or Jan for a lightweight, easy-to-deploy solution. Ollama is recommended for its ease of use and strong performance on the NVIDIA GeForce RTX 3090 Ti.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 3090 Ti

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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