OpenSquilla is an open-source Python AI agent with ML model routing, four-tier memory, and syscall-level sandbox isolation.

Stop thinking you need a $5,000 rig to run local AI — I finally ran a local AI on my old PC, and everything I believed was ...

Discover how a 12-year-old Raspberry Pi successfully runs a local LLM using Falcon H1 Tiny and 4-bit quantization.

In this tutorial, we explore kvcached, a dynamic KV-cache implementation on top of vLLM, to understand how dynamic KV-cache allocation transforms GPU memory usage for large language models. We begin ...

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 ...

Revenue cycle management company Ensemble Health Partners is working with clinical intelligence company Cohere to build the healthcare industry’s first RCM-native large language model. Four things to ...

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 ...

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 ...

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 ...