What are Microsoft Phi Models?
Microsoft Phi is a family of small language models (SLMs) that deliver surprisingly high performance at compact sizes. Unlike large LLMs like GPT-4, Phi models are optimized for scenarios where resource efficiency, latency, or privacy are more important than maximum capabilities.
The models are available via Azure AI Studio, Hugging Face, and as ONNX versions for local execution. They are suitable for edge deployment, mobile apps, or scenarios with limited GPU capacity.
Core Features
- Compact model sizes (3B to 14B parameters)
- High quality in reasoning and code generation
- Available in Azure AI Studio and as open weights
- ONNX export for local and edge inference
- Multimodal variants for text and image
Typical Use Cases
Edge AI: Deployment of language models on devices with limited resources like IoT gateways or local servers.
Mobile Applications: AI features in apps without cloud roundtrip for better latency and offline capability.
Cost Optimization: Inference-intensive workloads with lower costs than large foundation models.
Benefits
- Significantly lower inference costs than large LLMs
- Faster response times through compact size
- Local execution possible for privacy
- Open weights available for customization
Frequently Asked Questions
How do Phi models differ from GPT-4?
Phi models are smaller and more efficient but less capable for complex tasks. They are ideal for focused use cases, while GPT-4 is better suited for general, complex tasks.
Can I run Phi models locally?
Yes, Phi models are available as ONNX exports and can run on local hardware without cloud connection.
What sizes are available?
The Phi family includes models from 3B to 14B parameters, optimized for various performance and resource requirements.
How do I use Phi in Azure?
Phi models are available via Azure AI Studio and can be deployed via Azure OpenAI Service or as managed endpoints.
Integration with innFactory
As a Microsoft Solutions Partner, innFactory supports you with Phi models: evaluation for your use case, fine-tuning, edge deployment, and integration into existing applications.
