Gemma 2 is an open-source AI model developed by Google DeepMind. It can be deployed on-premises with the right GPU hardware for private, secure AI inference.

How much VRAM does Gemma 2 require?

VRAM requirements for Gemma 2 depend on the quantization level. Full-precision models need more VRAM, while quantized versions (Q4, Q5, Q8) can run on consumer GPUs. See our VRAM requirements table for specific recommendations.

Can I run Gemma 2 locally?

Yes. Gemma 2 can be run locally using frameworks like Ollama or vLLM. Petronella Technology Group builds GPU-accelerated workstations and servers optimized for local AI model deployment.

What GPU do I need for Gemma 2?

The recommended GPU depends on the model size and quantization. For smaller quantized versions, an AMD Radeon or NVIDIA RTX GPU with 16-24 GB VRAM may suffice. For full-precision or larger variants, enterprise GPUs like the AMD Instinct MI300X or NVIDIA A100 are recommended.

Does Petronella help deploy Gemma 2?

Yes. Petronella Technology Group provides end-to-end AI deployment services including hardware selection, system configuration, model optimization, and ongoing support. Contact us to discuss your Gemma 2 deployment needs.

Open-Source AI Model

Gemma 2

Name: Gemma 2
Author: Google DeepMind

Developed by Google DeepMind

Local AI Deployment Experts 24+ Years IT Infrastructure GPU Hardware In Stock

Key Capabilities

Best-in-class performance at each size tier
Knowledge-distilled from larger Gemini models
Strong safety training and alignment
Excellent for on-device and edge deployment
Efficient inference with grouped-query attention

VRAM Requirements by Quantization

Choose the right GPU based on your performance and quality needs.

Model / Quantization	VRAM Required
2B FP16	4GB
9B FP16	18GB
27B FP16	54GB
27B Q4	16GB

Use Cases

Gemma 2 (2B, 9B, 27B) can be deployed for enterprise AI applications including document processing, code generation, data analysis, and conversational AI. License: Gemma Terms of Use (permissive, commercial use allowed).

Run Gemma 2 with Petronella

PTG deploys Gemma 2 for organizations wanting Google-quality AI in small, efficient packages. Perfect for edge deployments, embedded systems, and environments with limited GPU budget.

Recommended Hardware

Model Size	Recommended GPU
2B	Any GPU with 4GB+ VRAM
9B	RTX 5080 (16GB)
27B	RTX 5090 (32GB) or RTX PRO 5000 (48GB)

Deploy Gemma 2 On-Premises

Our team builds GPU-accelerated systems configured and optimized for Gemma 2. Private, secure, and fully under your control.

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Gemma 2

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases