Hollywood loves a superpower. Not all involve capes or cosmic rays. Some are cognitive: characters who can remember everything. In movies and on TV, viewers repeatedly encounter those with ...

A new technical paper, “Rethinking Compute Substrates for 3D-Stacked Near-Memory LLM Decoding: Microarchitecture-Scheduling Co-Design,” was published by researchers at University of Edinburgh, Peking ...

Studies show THC can influence multiple stages of memory formation, shaping not just what we remember—but how accurately we remember it. New research suggests THC may do more than blur memory—it can ...

Forbes contributors publish independent expert analyses and insights. Analyzing tech stocks through the prism of cultural change. A team of Caltech mathematicians at PrismML just fit a full-power AI ...

The draft blog post describes a compute‑intensive LLM with advanced reasoning that Anthropic plans to roll out cautiously, starting with enterprise security teams. Anthropic didn’t intend to introduce ...

GPU memory is THE story. Ollama uses 13-19GB of unified memory during inference vs Atomic Chat's constant ~5GB. TurboQuant's 3-bit KV cache compression delivers its promised ~3.5x memory reduction.

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...

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 ...

Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...

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 ...