Can RTX 3070 Ti run Gemma 2 9B Instruct?

Yes — runs locally

~34 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

8 GB

Model size

9.2B

Best quant

Q4_K_M

VRAM needed

5.9 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q4_K_M quantization, which fits in 5.9 GB. Expected throughput is around 34 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 3070 Ti

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 3070 Ti with Q4_K_M quantization for Grade A performance at ~54 tok/sec.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.7 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 54 tokens per second, using about 5.9GB of VRAM. This leaves 2.1GB of VRAM for context, allowing for a practical context window of up to 8192 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized version of Gemma 2 9B Instruct (5.4GB file) from Hugging Face.

ollama pull bartowski/gemma-2-9b-it-GGUF:gemma-2-9b-it-Q4_K_M.gguf

3. Run it

ollama run gemma-2-9b-it-Q4_K_M --n-gpu-layers 12 --flash-attn --context-length 8192

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers 12 flag to offload some layers to CPU memory. Enable flash attention (--flash-attn) to reduce VRAM usage and improve speed. With these settings, you should achieve ~54 tok/sec while using around 5.9GB of VRAM, leaving 2.1GB for context.

Troubleshooting

Out of memory errors during inference.

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

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 was downloaded correctly and that there is sufficient disk space available. Try re-downloading the model.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more fine-grained control over optimizations. Jan is another option for those who prefer a web-based interface. For the NVIDIA GeForce RTX 3070 Ti, Ollama provides a balanced approach with ease of use and performance.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 3070 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 →