What if AI doesn’t need more RAM but better math?
Summary
An accessible explainer of Google's TurboQuant, PolarQuant, and QJL, detailing how high-dimensional vector compression can shrink KV cache memory without sacrificing accuracy. The piece argues this approach could ease the AI memory bottleneck, impact hardware demand, and alter the economics of memory stocks, with attention to edge inference and vector databases. It also notes data-oblivious properties and broad applications beyond LLMs.