Can RTX 4060 Ti 16GB run Gemma 2 9B Instruct?

Yes — runs locally

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

Your VRAM

16 GB

Model size

9.2B

Best quant

Q8_0

VRAM needed

9.7 GB

The verdict

The RTX 4060 Ti 16GB (16 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q8_0 quantization, which fits in 9.7 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 4060 Ti 16GB

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance at ~66 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.11 or later) with CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at ~66 tok/sec with 9.7GB of VRAM in use. The remaining 6.3GB of VRAM provides ample headroom for a full context window of 8192 tokens, ensuring smooth and responsive interactions.

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) from Hugging Face.

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 --context-length 8192

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 16 to fully utilize the GPU's 16GB VRAM. Enable --flash-attn for faster and more efficient attention computations. The model will use approximately 9.7GB of VRAM, leaving 6.3GB for context, which allows for a practical context window of up to 8192 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 8 or decrease the context length with --context-length 4096.

Slow token generation speed

Ensure that --flash-attn is enabled and try increasing the batch size with --batch-size 16.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for advanced customization options, or Jan for lightweight deployment. Each runtime has its strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 4060 Ti 16GB

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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