TurboQuant Changes the Math

Google just compressed AI's working memory by 6x. The market sold memory stocks. That logic is wrong.

TurboQuant is a training-free compression algorithm that quantizes LLM key-value caches to 3 bits with zero accuracy loss. 6x memory reduction. Up to 8x speedup on H100s. No retraining required. Works on any existing model. Drop it in, compress, deploy.

TurboQuant doesn't reduce the need for memory. It makes memory useful enough to unlock an entire category of applications that didn't clear the cost bar before. Same GPU, 6x more concurrent agents. Same phone, dramatically longer context windows.

What happens when inference becomes 6x cheaper overnight? They run 6x more agents.

This is the DeepSeek playbook, one layer up the stack. DeepSeek was about training efficiency. TurboQuant is about inference efficiency — and inference is where the bigger money will be.

Three unlocks: agents become viable on consumer devices (30B model on M5 MacBook). Hyperscaler economics flip from demo to production-viable. And agentic commerce becomes a real business — Google's UCP protocol combined with affordable inference creates consumer-scale agent deployment.

TurboQuant is software. Software runs on silicon. The inference silicon landscape is different from the training landscape — and that distinction matters for portfolio positioning.