Can RTX 5070 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 5070 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 5070 Ti

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 5070 Ti with Q8_0 quantization for 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, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

With the recommended settings, you can expect the model to run at ~66 tok/sec, utilizing 9.7GB of VRAM. Given the remaining 6.3GB of VRAM, you can achieve a practical context window of up to 8192 tokens, ensuring smooth and efficient inference.

1. Install runtimeOllama

pip install ollama
ollama config set cuda=True

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 --context-length 8192 --n-gpu-layers 32 --flash-attn
ollama chat bartowski/gemma-2-9b-it-GGUF:q8_0

4. Optimize for RTX 5070 Ti

For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, use --n-gpu-layers 32 to offload layers to the GPU, enable --flash-attn for faster attention computations, and consider using tensor parallelism if you plan to run multiple instances. This configuration will utilize approximately 9.7GB of VRAM, leaving 6.3GB for context and other operations.

Troubleshooting

Out of memory error during inference

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

Slow inference speed

Ensure CUDA is properly configured and enabled with 'ollama config set cuda=True'. Also, check if --flash-attn is enabled.

Model fails to load

Verify the model file integrity by re-downloading it using 'ollama pull bartowski/gemma-2-9b-it-GGUF:q8_0'.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more customization or specific features. LM Studio is ideal for a GUI-based approach, llama.cpp offers low-level control, and Jan is suitable for distributed setups. However, Ollama provides a balanced and user-friendly experience for most users on the NVIDIA GeForce RTX 5070 Ti.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 5070 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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