What is Quantization and How to Run Big Models on Small GPUs?
A technical introduction to model quantization (GGUF, EXL2) and how it enables local AI on consumer hardware.
Large models (like Llama 3 70B) are too big to fit into the memory of a standard home GPU. Quantization is the technology that "shrinks" these models so they can run on your local machine.
Reducing Precision, Maintaining Intelligence
Standard models are stored in FP16 (16-bit) precision. Quantization reduces this to 8-bit, 4-bit, or even 1.5-bit. While this sounds like a massive loss, the model's "intelligence" often stays remarkably intact. By shrinking the weights, a model that previously required 140GB of VRAM can fit into 35GB, making it possible to run "frontier-class" AI on a high-end consumer PC.
Choosing the Right Format
There are several popular quantization formats, such as GGUF (for CPU/Apple Silicon), EXL2 (for high-speed NVIDIA inference), and AWQ. Choosing the right format depends on your hardware and your performance needs. Mastering these formats is essential for any developer looking to build private, local-first AI applications.