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 ...

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 ...

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 ...

Shawn Shen believes that AI will need to remember what it sees in order to succeed in the physical world. Shen’s company Memories.ai is using Nvidia AI tools to build the infrastructure for wearables ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...

On March 27, the US International Trade Commission (ITC) launched an investigation into memory chip imports by SK Hynix Inc. and KIOXIA Holdings Corporation following a patent complaint... Samsung ...

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 ...

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 ...

Memory is the faculty by which the brain encodes, stores, and retrieves information. It is a record of experience that guides future action. Memory encompasses the facts and experiential details that ...