TechnicalAI glossary
Quantisation
Compressing a model by reducing the precision of its numbers so it can run on modest hardware, with minimal loss of quality.
In other words
The MP3 of AI models: lighter, almost as good.
Alyriaz articles that mention it
- What is an exaflop? Measuring computing power, and why comparisons mislead
Computing power announcements are multiplying in exaflops. We explain this unit, why the same hardware can show very different figures, and how to read these announcements.
- Apple changes boss on the day its AI lag becomes a problem
John Ternus becomes Apple's CEO on 1 September 2026. We analyse the AI portfolio he inherits, caught between a privacy-focused stance and functional lag.
- Three years of AI in the enterprise, and almost no measurable productivity gains
A survey by the US National Bureau of Economic Research reports that the vast majority of executives surveyed see no effect of AI on their productivity or on employment. We analyse this puzzling result.
- What is "quantization"? The compression that fits a giant model on your machine
Quantization involves storing a model's parameters with less precision, which significantly reduces its size and hardware requirements. We explain the mechanism, common levels, and where the acceptable limit lies.
- Kimi K3 has kept its promise: the weights of the world's largest open model are now online. And they weigh in at 1.4 terabytes.
On 27 July 2026 at midnight UTC, Moonshot AI released the full weights of Kimi K3, the largest open model ever distributed. They take up around 1.4 terabytes. We explain what this release actually changes, and why most users will still go through a provider.
- Running an AI on your own computer: what it changes, and what it really takes
Running an AI directly on your computer has become accessible: total privacy, no cost per query, no dependency. We explain what it concretely requires in terms of hardware, which tools to use, what to really expect, and why an open model isn't necessarily one you can run at home.