Can RTX 3080 Ti run Gemma 2 9B Instruct?

Yes — runs locally

~46 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

12 GB

Model size

9.2B

Best quant

Q5_K_M

VRAM needed

6.7 GB

The verdict

The RTX 3080 Ti (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 46 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Google's efficient 9B model. Great performance-to-size ratio.

Setup tutorial: Gemma 2 9B Instruct on RTX 3080 Ti

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 3080 Ti with Grade S performance at ~71 tok/sec using the Q5_K_M quantization.

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 11.8 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 you with 5.3GB of VRAM 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 gemma-2-9b-it-GGUF:q5_k_m --interactive
ollama chat gemma-2-9b-it-GGUF:q5_k_m

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 50 to balance between speed and memory usage. Enable flash attention (--flash-attn) to reduce memory consumption and improve inference speed. With 6.7GB VRAM used by the model, you have approximately 5.3GB of VRAM left for context, allowing for a practical context window of around 4096 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 40.

Slow inference speed

Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no issues with the Ollama installation. Try reinstalling Ollama and pulling the model again.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a user-friendly interface and advanced features, while llama.cpp offers more control over quantization and optimization. Jan is suitable for lightweight deployments where resource efficiency is critical. For the NVIDIA GeForce RTX 3080 Ti, Ollama provides a balanced solution with ease of use and good performance.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 3080 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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