Can RTX 4090 run Gemma 2 9B Instruct?

Yes — runs locally

~96 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 4090 (24 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q8_0 quantization, which fits in 9.7 GB. Expected throughput is around 96 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 4090

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 4090 with Ollama using the Q8_0 quantization. Expect Grade S performance at ~98 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 525.60 or later) installed. Additionally, install CUDA 11.8 or later.

Expected performance

With the Q8_0 quantization, you can expect a token generation rate of approximately 98 tokens per second, utilizing around 9.7GB of VRAM. This leaves about 14.3GB of VRAM for context, allowing for a practical context window of up to 8192 tokens without running out of memory.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

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

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

3. Run it

ollama run bartowski/gemma-2-9b-it-GGUF:q8_0 --interactive
ollama chat bartowski/gemma-2-9b-it-GGUF:q8_0

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 48 to utilize the full 24GB VRAM effectively. Enable flash attention with --flash-attn to speed up inference. Tensor parallelism can also be used to further optimize performance, but it is generally not necessary for this model and GPU combination.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 32 or 24.

Slow token generation rate

Enable flash attention with --flash-attn and ensure that your CUDA installation is up to date.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no issues with the Ollama installation. Re-run the 'ollama pull' command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more advanced customization options. Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended runtime for this GPU and model combination.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 4090

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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