Google published research on TurboQuant, a quantisation method that compresses AI model weights to a fraction of their original size while maintaining most of their accuracy. This sounds technical. The implication is simple: AI inference gets cheaper.
What Quantisation Is
Neural network weights are stored as floating point numbers, 32-bit or 16-bit values. Quantisation converts these to lower-precision formats (8-bit, 4-bit) using smart rounding techniques. Smaller numbers mean less memory, faster computation, lower cost. The risk is accuracy loss, if you round too aggressively, the model starts making worse predictions.
What TurboQuant Claims
The paper claims near-zero accuracy loss at 8-bit quantisation and acceptable loss at 4-bit. If this holds in production deployment, it means Gemini-class models can run at a fraction of current inference costs.
Why This Matters for Businesses
API costs for AI services have been dropping consistently for two years. TurboQuant and similar research are part of why. The practical effect: the AI tools you build today will be cheaper to run in 18 months, without you having to change anything.