Imagine a world where AI models respond instantly, no matter how complex the task. That's the promise of AQUA, a groundbreaking new system for making AI inference faster and more responsive in multi-GPU environments. The challenge with today's large language models (LLMs) is that they're memory hogs. Running multiple models on a server with several GPUs often leads to bottlenecks, with some models starving for memory while others have plenty to spare. Think of it like a highway with some lanes jammed while others are empty. What if we could dynamically redirect traffic to those open lanes? That's exactly what AQUA does. By cleverly managing memory across interconnected GPUs, AQUA allows memory-starved models to borrow resources from their compute-bound neighbors. This innovative approach significantly reduces the overhead of swapping memory, a major culprit in slowing down AI inference. The result? Up to 4X faster response times and a 6X boost in throughput. This isn’t just about speed; it’s about unlocking new possibilities for AI. With AQUA, interactive applications like chatbots become snappier, and complex tasks like long-prompt inference are handled with ease. While still in its early stages, AQUA represents a significant leap forward in AI infrastructure, paving the way for more responsive and efficient AI experiences.