🧩 AI Course 4: “Models on a Diet: Pruning, Distillation, and Quantization"

🔹 Objective: Learn to compress models so they are fast, lightweight, and suitable for mobile devices or production.

📌 Introduction: Why do models need a diet?

In recent years, artificial intelligence models — especially large language models (LLMs) and vision models — have grown exponentially in size. Models like GPT-3 (175 billion parameters), Llama 3 (up to 400B), or Stable Diffusion XL (2.6B) represent impressive technical achievements, but also pose enormous practical challenges.

Training and running these models requires expensive infrastructure: high-performance GPUs, massive VRAM memory, high energy consumption, and inference times that can be unacceptable in real-world environments. For example, a 7B-parameter model in FP32 format may occupy over 28 GB of memory just to load. This makes it unfeasible for:

• Mobile or edge devices (phones, tablets, IoT).
• Web applications with thousands of concurrent users.
• Startups or teams with limited budgets.
• Scenarios where latency must be minimal (real-time assistants, games, embedded systems).

This is where model compression comes in.

Compression is not a luxury; it is a strategic necessity. It’s not just about saving space, but about making real-world AI deployment viable. A compressed model can:

• Reduce its memory footprint by up to 8x.
• Accelerate inference by 4x or more.
• Drastically decrease energy consumption.
• Enable execution on modest hardware (even without a GPU).
• Lower cloud infrastructure costs.
• Improve end-user experience (faster responses).

This course will teach you three fundamental compression techniques: Pruning, Knowledge Distillation, and Quantization. Each addresses the problem from a different angle, and they are often combined to achieve optimal results.