Can RTX 5090 run Gemma 2 9B Instruct?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Gemma 2 9B Instruct comfortably using the Q8_0 quantization, which fits in 9.7 GB. Expected throughput is around 114 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 2 9B Instruct on an NVIDIA GeForce RTX 5090 with a Grade S performance, using the Q8_0 quantization for ~131 tok/sec speed.
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 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect a performance of ~131 tok/sec, utilizing 9.7GB of VRAM. The remaining 22.4GB of VRAM provides ample headroom for a context window of up to 8192 tokens, ensuring smooth and efficient inference.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized model (9.2GB file) from the Hugging Face repository.
ollama pull bartowski/gemma-2-9b-it-GGUF:gemma-2-9b-it-Q8_0.gguf3. Run it
ollama run gemma-2-9b-it-Q8_0 --interactive
ollama chat gemma-2-9b-it-Q8_04. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size 2. This configuration will utilize 9.7GB of VRAM, leaving 22.4GB for context and other operations, allowing for a large practical context window.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the batch size.
Slow inference times
Ensure that flash attention is enabled with --flash-attn and that the model is fully loaded into GPU memory.
Model fails to load
Check 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 graphical interface, llama.cpp for lightweight and portable deployment, or Jan for advanced customization options. Each runtime has its strengths, but Ollama provides a balanced and user-friendly experience, especially for interactive use on powerful GPUs like the RTX 5090.
Other models that run great on RTX 5090
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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