Imagine a world where your AI assistant responds instantly, where search results appear before you even finish typing, and where complex AI-powered applications run smoothly on your phone. This is the promise of faster AI inference, and researchers are constantly pushing the boundaries to make it a reality. One of the biggest bottlenecks in achieving this speed is the way large language models (LLMs), like the ones powering ChatGPT and Bard, handle information. These models rely on a mechanism called "attention," which helps them understand the relationships between words in a sentence. However, when scaled to massive datasets and complex tasks, this attention mechanism becomes computationally expensive, especially during inference, the process of actually using the model to generate text or answer questions. A new research paper introduces "Kraken," an innovative architecture designed to tackle this challenge head-on. Kraken reimagines the traditional transformer architecture, the foundation of most modern LLMs, by introducing a fixed degree of inherent parallelism. This means that instead of processing information sequentially, Kraken breaks down the task and distributes it across multiple devices, like the GPUs in a powerful computer. This allows for overlapping communication and computation during the model’s operation, drastically reducing the time it takes to generate the first token (TTFT), a crucial metric for measuring inference speed. In benchmarks using NVIDIA's TensorRT-LLM library, Kraken demonstrated remarkable speed improvements, slashing TTFT by an average of 35.6% across various model sizes and context lengths. This breakthrough is particularly significant for real-world applications where low latency is critical, such as chatbots, real-time translation, and augmented search. While Kraken requires significant computational resources for initial training, the potential benefits for a wide range of applications are undeniable. It represents a crucial step towards realizing the full potential of LLMs, paving the way for a future where AI is not just powerful, but also incredibly fast and responsive.