Can RTX 5080 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 5080 (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 5080

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

TL;DR

Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 5080 with Ollama using the 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 should expect ~66 tok/sec performance with 9.7GB VRAM in use, leaving 6.3GB for context. This allows for a practical context window of up to 8192 tokens, making it suitable for long-form content generation and complex tasks.

1. Install runtimeOllama

curl -L 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) 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 48 --flash-attn
ollama chat gemma-2-9b-it-Q8_0

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 48 to utilize most of the GPU memory while leaving some headroom. Enable --flash-attn to speed up attention computations. With these settings, you can achieve ~66 tok/sec with 9.7GB VRAM in use, leaving 6.3GB for context, allowing for a practical context window of up to 8192 tokens.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 32 or enable --cpu-offload to offload some layers to CPU.

Slow token generation rate

Ensure --flash-attn is enabled and check your CUDA installation for any issues.

Model fails to load

Verify the integrity of the downloaded model file and try re-downloading it.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 5080.

Full Gemma 2 9B Instruct details →

Other models that run great on RTX 5080

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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