Large language models (LLMs) have revolutionized how we interact with technology, but their massive size presents significant challenges for deployment. Running these behemoths requires substantial computing power and energy, limiting accessibility and increasing costs. One popular approach to shrink LLMs down to size is weight-only quantization, a technique that reduces the precision of the model's weights (like shrinking the number of decimal places used in its calculations). This frees up memory and bandwidth but shifts the bottleneck to the activations—the intermediate results generated during the model's computations. A new approach called "Anda" tackles this activation bottleneck head-on. Researchers explored how sensitive LLM accuracy is to the precision of these activations, discovering that different parts of the model have varying tolerances. Based on this, they developed Anda, a variable-length data format that allows for customized precision for different parts of the LLM. Imagine tailoring the number of decimal places used in each part of a complex calculation to optimize for both speed and accuracy—that's Anda in essence. This clever trick, combined with a specialized hardware architecture that operates with Anda natively, offers dramatic performance improvements. Anda-enhanced systems are over twice as fast and more than three times as energy-efficient as current state-of-the-art GPU systems, all while maintaining acceptable accuracy levels. This breakthrough could democratize access to powerful LLMs, enabling their deployment on smaller, more energy-efficient devices, and opening exciting new possibilities for AI-powered applications everywhere.