Google announces ‘super efficient’ compression algorithm for AI

Researchers at Google Research announce TurboQuant, a super-efficient memory compression algorithm for AI. The technology world quickly called this “Pied Piper”, the name of the startup with the magical compression algorithm in the famous TV series. Silicon Valley.

Google’s algorithm is expected to solve the bottleneck of artificial intelligence. Currently, the weakness of large language models is that they consume too much RAM memory. To process data, AI needs KV cache (key-value cache). As the data becomes more complex, the cache KV becomes larger, leading to performance bottlenecks and costly operating costs.

Normally, to reduce capacity, quantization techniques can be used, but in return the AI ​​output quality is reduced. TurboQuant was born to solve this problem. Experimental results show that the algorithm reduces memory capacity by six times and increases processing speed by eight times while still maintaining accuracy.

To achieve this milestone, Google combined two new techniques, PolarQuant. Instead of representing data in the traditional XYZ coordinate system (like going three steps East, four steps North), the new algorithm switched to polar coordinates, like going 5 steps at a 37 degree angle. This method helps shorten information, saving significant storage space. Next is QJL (Quantized Johnson Lindenstrauss), a 1-bit error correction layer. After compression with PolarQuant, small errors may appear. QJL will correct these errors to ensure the AI ​​still correctly identifies what data is important.

Matthew Prince, CEO of Internet infrastructure provider Cloudflare, commented that this could be Google’s “DeepSeek moment”, in terms of optimization capabilities that help AI run smoothly on poor hardware at a cheaper cost.

TurboQuant is still in the testing phase and is expected to be presented in detail at next month’s ICLR 2026 conference. The algorithm does not help completely solve the global RAM shortage because it only focuses on the inference process instead of the training process, but opens up the future for mobile AI. With smartphone hardware limitations, TurboQuant can support virtual assistants to process complex data right on the device without sending it to the cloud.