Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during inference grows with every token generated, forcing operators to choose between ...

Google's TurboQuant can dramatically reduce AI memory usage. TurboQuant is a response to the spiraling cost of AI. A positive outcome is making AI more accessible by lowering inference costs. With the ...

Qdrant is launching version 1.18 of its platform, introducing TurboQuant, a new quantization method developed by Google Research. According to the company, TurboQuant applies a fast Hadamard rotation ...

At its core, TurboQuant compresses the key-value (KV) cache -- the short-term working memory AI models use during inference -- by converting data vectors into polar coordinates and subsequently ...

AI has a growing memory problem. Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced 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 ...

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 is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized ...