Quantization - How to “Shrink” Models Without Hurting Results

AI models, especially large ones (LLMs), consume enormous amounts of memory and compute. To run them efficiently, you need a smart way to shrink them - without breaking accuracy.

This is where quantization comes in.

What Is Quantization, Really?

Quantization is a technique that converts very precise numbers (usually float32) into a “coarser” version - for example int8, or even int4.

In simple terms: we’re reducing the resolution of the model’s weights. Instead of storing 32 bits per value, we store just 8 bits - 4x less data.

Why Does This Matter?

Memory Savings

A huge model that takes up 80GB in its original form can shrink to around 20GB after quantization. The implication: you can run it on weaker hardware, or handle a larger volume of concurrent requests.

Significant Compute Speedup

Operations on small integers (int8) run much faster on modern processors and accelerators, leading to a noticeable improvement in inference latency and throughput.

Lower Power Consumption

Fewer bits = fewer memory read/write operations = less energy.

But What’s the Cost?

The lower the resolution, the more accuracy the model loses - because we lose information in the numbers.

That’s why there are two main approaches to quantization:

• Post-training quantization - the conversion is done after the model has already been trained. Simple, fast, but less accurate.
• Quantization-aware training (QAT) - the model is trained in advance “under the assumption” that its weight values will be quantized. This preserves accuracy, but requires more work.

A Simple Example

Quantizing to int8 doesn’t turn a decimal weight into a “more rounded” decimal number - it turns it into an integer, within a fixed range (say, -128 to 127), according to a conversion factor (scale).

For example, a weight w = 0.8374 could become w_q = 107 (an integer), with an appropriate scale used to reconstruct an approximation of the original value.

This preserves the relative meaning of the values - while storage and execution happen with integers, which are far cheaper. (Exactly how the scale and conversion are computed is the topic of the next post.)

So Why Is Everyone Talking About This?

As models grow, the main bottleneck isn’t accuracy - it’s resources. Quantization makes it possible to run large models on smaller, cheaper hardware, and bring AI capabilities to edge devices too - like phones, cars, or smart cameras.

Summary

Quantization isn’t just “a trick for saving memory.” It’s one of the key technologies that let AI move from the lab - into the real world.