research.google

TurboQuant looks like a pretty massive deal for running local models efficiently. The core issue they are tackling is the memory bottleneck caused by the key value cache during generation. When you are doing long context inference storing all those high dimensional vectors eats up VRAM extremely fast. Traditional vector quantization helps but usually introduces memory overhead because you have to store scaling factors or constants in full precision for every small block of data. That overhead can easily add an extra bit or two per parameter which ruins the compression targets people are aiming for.

TurboQuant looks like a pretty massive deal for running local models efficiently. The core issue they are tackling is the memory bottleneck caused by the key value cache during generation. When you are doing long context inference storing all those high dimensional vectors eats up VRAM extremely fast. Traditional vector quantization helps but usually introduces memory overhead because you have to store scaling factors or constants in full precision for every small block of data. That overhead can easily add an extra bit or two per parameter which ruins the compression targets people are aiming for.

Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache, a high-speed “digital cheat sheet” that stores frequently used information under simple labels so a computer can retrieve it instantly without having to search through a slow, massive database. Vector quantization is a powerful, classical data compression technique that reduces the size of high-dimensional vectors…

Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache, a high-speed “digital cheat sheet” that stores frequently used information under simple labels so a computer can retrieve it instantly without having to search through a slow, massive database.