AI

Mixture of Experts architecture delivers larger-model capability while activating only a fraction of the model’s parameters per token. That makes it a strong fit for local AI, but the full model still has to remain accessible in memory. Here’s why that matters and...

Find out how memory optimization and infrastructure design can expand AI access while improving efficiency and sustainability.   Introduced in 2015, the United Nations’ Sustainable Development Goals (SDGs) provide a global framework for addressing some of the...

Extend effective GPU memory and run more-capable AI workloads on existing local systems by rethinking how memory is managed across the stack.   As AI adoption accelerates, so does pressure on the infrastructure that supports it. Over the past year, memory...

Agentic AI workloads demand more memory than traditional AI, especially when running locally. As models grow and agents maintain long-running state, memory becomes the primary bottleneck. This article explains how aiDAPTIV extends effective AI memory to enable...