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 ...

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 has unveiled a new technique that could dramatically reduce the amount of memory required to run artificial intelligence (AI) models. The breakthrough, called TurboQuant, was announced by ...

Google’s announcement of TurboQuant is weighing on the share prices of memory companies, as the technology is expected to cut artificial intelligence (AI) models’ memory usage to about one-sixth of ...

Google Research published TurboQuant on Tuesday, a training-free compression algorithm that quantizes LLM KV caches down to 3 bits without any loss in model accuracy. In benchmarks on Nvidia H100 GPUs ...

Google's (GOOG)(GOOGL) TurboQuant, a compression algorithm that optimally addresses the challenge of memory overhead in vector quantization, will likely lead to the usage of more intensive AI ...

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 ...

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 ...

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 ...