Large language models (LLMs) are the brains behind many AI services, but running them efficiently is a challenge. They need serious computing power, and even the fastest GPUs often struggle. Why? LLMs have diverse needs. Some parts, like generating text, are memory-bound – meaning they spend most of their time waiting for data. Other tasks, like summarizing large chunks of text, are compute-bound, demanding raw processing power. So, how do you build a system that handles both well? Researchers have introduced IANUS, a new architecture that combines a Neural Processing Unit (NPU) with Processing-in-Memory (PIM). Think of the NPU as a specialized processor designed for AI tasks, and PIM as a type of memory that can perform calculations directly within it. This combination lets IANUS tackle the diverse workload of an LLM much more efficiently. One of IANUS's key innovations is its *unified memory system*. Instead of separate pools of memory for the NPU and PIM, IANUS uses a shared pool. This cuts down on the need to move data around, which is a major performance bottleneck. However, sharing memory also creates new challenges, as regular memory requests and PIM calculations can clash. To solve this, IANUS employs clever scheduling to manage these conflicts and keep everything running smoothly. Simulations show IANUS significantly outperforming both GPUs and other specialized accelerators, speeding up some LLM tasks by a factor of six. As a proof-of-concept, the researchers even built a prototype using off-the-shelf components and an FPGA, proving IANUS isn’t just a theory. This kind of hybrid architecture, combining specialized processors and memory, could be the key to unlocking the full potential of LLMs and making AI services faster and more efficient.