Explained · 2 min
What is quantization?
How AI models are shrunk to fit on smaller computers, and what it costs.
Written by AI and checked against our editorial standards. Not reviewed by a human editor.
Quantization is a way to make an AI model smaller by storing its numbers with less detail. It is one of the main reasons large models can run on gaming PCs and laptops.
A model is a huge file of numbers called weights. Each weight is often stored with 16 bits, sometimes 32, which is a lot of detail. Quantization rounds each weight so it fits in fewer bits, such as 8 or 4. A model stored with 4 bits per weight needs about a quarter of the memory it did with 16.
Think of it like this: a photo saved as a smaller JPEG. It takes far less space and still looks right, but zoom in and some fine detail is gone. Where the comparison fails: you can’t see the loss in a model. It can get worse on some tasks with no visible sign.
You will see the formats in model names: FP8 and INT8 use 8 bits, while INT4 and NVFP4 use 4. GGUF is a common file format for quantized models in tools like llama.cpp and Ollama. Some newer methods go down to 1 or 2 bits, but those usually need extra care to keep quality up.
The trade-off is simple. Fewer bits means less memory and often faster answers. It also means some loss of accuracy, which is small at 8 bits and grows below 4. Labs often test their quantized versions and publish the scores, so you can compare.
Why it matters to you: quantization decides which models fit on your hardware. When a model says it runs on an 8 GB or 24 GB graphics card, it is usually a quantized version. Test it on your own tasks, because the loss is not the same everywhere.
Learn more: Hugging Face Transformers docs: Quantization overview