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 ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

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 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 ...

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 ...

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 ...

For about four years now, AMD has offered special “X3D” variants of its high-end desktop processors with an extra 64MB of L3 cache attached, an addition that disproportionately benefits games. AMD ...

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 ...