RAM prices are enough to make you choke on your toast, so Google Research has turned up with TurboQuant to cram LLMs into less memory. TurboQuant is pitched as a compression trick for the key-value ...

Is TurboQuant a silicon bullet to solve the RAM crisis? No, it isn't, and if you were hoping that the compression algorithm that Google recently announced would be a major turning point for AI ...

Long context AI no longer means using data centres thanks to Tether acting swiftly on groundbreaking research from Google.

TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

Google, which has been at the forefront of artificial intelligence (AI) innovation, has presented a solution to the ongoing memory semiconductor shortage. As the shortage and bottleneck issues ...