⚙️ Provisioning - Preparing the Ground Before Running Models

Before an AI model even starts running, the environment it will work in needs to be “set up.” This stage is called Provisioning - essentially the process of allocating and preparing computing resources.

What Does It Include?

Provisioning determines where and under what conditions the model will run. This includes:

• 📦 Allocating servers, GPUs, or CPUs
• 💾 Connecting to data storage
• 🌐 Configuring networks, IP addresses, and permissions
• 🧠 Loading models into memory

Think of it like preparing a stage before a performance - before the actors (models) come on, you need lights, microphones, power, and order.

Why is It Important?

If provisioning isn’t done correctly:

• The model will load slowly.
• Inference requests might crash.
• Computing costs will skyrocket.

On the other hand, smart provisioning can save time and money, especially in large systems like data centers.

Common Types of Provisioning

Static Provisioning

Resources are allocated in advance and remain fixed - suitable for stable environments with predictable loads.

Dynamic (or Auto) Provisioning

Resources are allocated and released automatically as needed - if more inference requests come in, the system “adds computing power” on its own.

Serverless Provisioning

The user doesn’t see any servers at all - the service itself manages all allocation behind the scenes.

A Real-World Example

When you send a request to a GPT or BERT model, this happens behind the scenes:

1️⃣ Kubernetes or another management system detects a new request. 2️⃣ It allocates a free GPU. 3️⃣ Loads the model into memory. 4️⃣ Only then does the actual inference begin.

All of this - real-time provisioning.

Bottom Line

Provisioning is the stage where the system “gets ready” before work begins. In smart AI systems, it happens automatically - in seconds - but without it, no model would run.