Can RTX 5060 Ti 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 5060 Ti (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 5060 Ti

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 5060 Ti with Ollama using the Q8_0 quantization. Expect Grade S performance at ~66 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 525.60.11 or later), and CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at ~66 tok/sec, using 9.7GB of VRAM. The remaining 6.3GB of VRAM provides ample headroom for a context window of up to 6000 tokens, ensuring smooth and efficient operation.

1. Install runtimeOllama

curl -fsSL https://ollama.ai/install.sh | sh
ollama install

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

3. Run it

ollama run gemma-2-9b-it-Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat gemma-2-9b-it-Q8_0

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn to reduce memory usage and improve speed. With 9.7GB VRAM used by the model, you will have approximately 6.3GB of VRAM left for context, allowing for a practical context window of around 6000 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or decrease the context window size.

Slow token generation

Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.

Model fails to load

Verify that the model file was downloaded correctly and that there is sufficient disk space available.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over specific features or optimizations. LM Studio is ideal for a graphical interface, llama.cpp offers fine-grained control over quantization and performance settings, and Jan is suitable for lightweight, low-resource environments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 5060 Ti.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 5060 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.

Want personalized recommendations for your exact setup? Detect my hardware →