In this article we will share two simple ideas from AI engineering, quantization and distillation, and why they matter for the future of AI video generation. By reading this complete blog you will learn three things: first, what quantization actually means in plain English, second, what distillation means and how it is different, and third, a real example of these techniques already running on an ordinary computer today.
What Is Quantization
Quantization is a way of shrinking an AI model by storing its internal numbers with less precision. A full size AI model normally stores its calculations using very precise decimal numbers, which takes a large amount of memory and computing power. Quantization rounds those numbers to a simpler format, similar to rounding 3.14159265 down to 3.14. The model becomes smaller and faster to run, and in most cases the drop in quality is small enough that a normal person cannot notice the difference.
What Is Distillation
Distillation is a different idea. Instead of shrinking one model, you train a smaller model to copy the behavior of a much larger one. The large model acts as a teacher, and the smaller model learns to produce similar answers with far fewer parameters. A distilled model will usually not be as capable as the original on every task, but for narrow, repeated tasks, it can perform close enough while using a fraction of the resources.
A Real Example: How MyDocstor Uses a Quantized Model
This is not just a theory. The DocstorDesk desktop app, MyDocstor's AI video tool, runs a quantized version of a 1.5 billion parameter language model directly on a regular Windows or Mac computer, with no cloud server and no expensive GPU required. The full precision version of that model would be too large and too slow for a normal laptop, but the quantized version runs comfortably in the background while generating a video. This is a working, real world example of the same principle that is expected to eventually make far more AI features affordable to run without cloud infrastructure.
Why This Trend Points Toward Free Video Generation Later
The pattern across the AI industry has been consistent for years: a capability starts out expensive and cloud only, then quantization, distillation, and better hardware slowly bring it down to something that can run locally on consumer devices. Voice recognition, image generation, and now smaller language models have all followed this path. Full AI video generation is still in the expensive, cloud only stage today, which is part of why models like Sora struggled with cost, as we covered in our post on the real cost of pixel-generating AI video. There is no guarantee of an exact timeline, but the direction is clear.
Try What Is Already Possible Today
You do not need to wait for full AI video generation to become cheap to start creating now. Lightweight tools that combine AI voiceover with stock footage matching already give you a practical way to produce videos today. Try the AI video tool, or explore all the free utilities on the MyDocstor home page.