Phi-4 is an open-source AI model developed by Microsoft Research. It can be deployed on-premises with the right GPU hardware for private, secure AI inference.

How much VRAM does Phi-4 require?

VRAM requirements for Phi-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 Phi-4 locally?

Yes. Phi-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 Phi-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 Phi-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 Phi-4 deployment needs.

Open-Source AI Model

Phi-4

Name: Phi-4
Author: Microsoft Research

Developed by Microsoft Research

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

Key Capabilities

Punches far above its weight on reasoning benchmarks
Outperforms many 70B models on math and logic tasks
Small enough to run on a single consumer GPU
MIT license for unrestricted commercial use
Excellent for edge deployment and embedded AI

VRAM Requirements by Quantization

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

Model / Quantization	VRAM Required
FP16	28GB
Q4	8GB

Use Cases

Phi-4 (14B) can be deployed for enterprise AI applications including document processing, code generation, data analysis, and conversational AI. License: MIT License.

Run Phi-4 with Petronella

PTG deploys Phi-4 for edge AI and resource-constrained environments. Ideal for small businesses, edge devices, and compliance environments where a smaller model footprint reduces attack surface.

Recommended Hardware

Model Size	Recommended GPU
FP16	RTX PRO 4000 (24GB) or RTX 5090 (32GB)
Q4	RTX 5080 (16GB) or any 8GB+ GPU

Deploy Phi-4 On-Premises

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

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Phi-4

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases