Gemma 4 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 4 require?

VRAM requirements for Gemma 4 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 4 locally?

Yes. Gemma 4 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 4?

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 4?

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 4 deployment needs.

Open-Source AI Model

Gemma 4

Name: Gemma 4
Author: Google DeepMind

Developed by Google DeepMind

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

Key Capabilities

Agentic workflows with native function calling and app navigation
Multimodal reasoning across text, audio, and visual inputs
140 language support — broadest multilingual coverage in open models
Edge deployment on mobile, IoT, Raspberry Pi, Jetson Nano (E2B/E4B)
Arena AI 1452, AIME 2026 89.2%, LiveCodeBench 80.0%, GPQA-Diamond 84.3%

VRAM Requirements by Quantization

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

Model / Quantization	VRAM Required
E2B FP16	4GB
E4B FP16	8GB
26B FP16	52GB
26B Q4	16GB
31B FP16	62GB
31B Q4	18GB

Use Cases

Gemma 4 (E2B, E4B, 26B, 31B) 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 4 with Petronella

PTG deploys Gemma 4 for organizations needing frontier multimodal AI with agentic capabilities. The 31B flagship delivers unprecedented intelligence-per-parameter, while E2B/E4B variants enable real-time edge AI on mobile and IoT devices — ideal for air-gapped CMMC environments.

Recommended Hardware

Model Size	Recommended GPU
E2B	Any GPU with 4GB+ VRAM or CPU-only
E4B	Any GPU with 8GB+ VRAM
26B	RTX 5090 (32GB) or RTX PRO 5000 (48GB)
31B	RTX 5090 (32GB) or RTX PRO 6000 (96GB)

Deploy Gemma 4 On-Premises

Our team builds GPU-accelerated systems configured and optimized for Gemma 4. Private, secure, and fully under your control.

Talk to an AI Infrastructure Expert Browse AI Hardware

Gemma 4

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases