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Behind every powerful artificial intelligence (AI) model sits memory. It is what allows systems to recall context, process queries, and deliver responses in real time. But the more advanced AI becomes, the heavier its memory demands get. That means higher costs, bigger infrastructure, and more strain on hardware. So, when someone finds a way to cut that dependency, it doesn’t just improve efficiency, but also changes the equation.
That shift is starting to take shape with Google’s latest move. The company unveiled TurboQuant, a compression algorithm that reduces the memory AI systems actually need. It compresses key-value cache down to just three bits, and early results show it can shrink memory usage dramatically – up to 6x – while keeping performance intact. In effect, AI models could soon run with far less reliance on large memory pools.
