TechnicalAI glossary
GPU
Graphics processors (Nvidia leading the way) have become the backbone of AI: they perform millions of calculations in parallel, making them perfect for training models.
In other words
A thousand cashiers in parallel rather than one very fast one.
Alyriaz articles that mention it
- China is mass-producing its own memory, closing the last remaining gap
Chinese manufacturer CXMT has announced mass production of its fifth-generation DRAM memory, already integrated into high-end smartphones. We analyse what this link changes in the supply chain.
- GLM-5.3-Flash runs on 100,000 Chinese accelerators, at a comparable cost
Z.ai has announced that its flagship open model is deployed on more than 100,000 Chinese accelerators, with throughput tripled in two weeks. We analyse what this claim implies if it is confirmed.
- The debate over the slowdown erupts in public
Dario Amodei's call to slow down triggered responses from the heads of Microsoft, OpenAI and xAI within four days, a legislative proposal and political reactions. We analyse this shift of the debate into the public sphere.
- The week of extraordinary claims
Week in review, 7–13 September 2026: Navier-Stokes, attribution disputes, Mistral, Suno v6, and data centres leaving the grid.
- Washington accuses Chinese labs of siphoning intelligence from American models
US agencies accuse Chinese labs of extracting the capabilities of American models at scale. We explain the technique in question and why it is hard to prevent.
- Running AI on the machines that sleep at home
Nvidia has introduced a local clustering tool that lets you use idle Macs and PCs to run models. We explain how it works, its limitations, and why it arrives at the right time.