Operating large language model (LLM) services like ChatGPT requires a server infrastructure on the scale of tens of thousands of units. However, constructing actual equipment every time a new AI semiconductor or system architecture needs to be verified incurs massive costs and time.
A research team at our university has developed a "virtual testbed" that can pre-verify performance and efficiency inside a computer before building an actual large-scale AI server.
The research on a large language model (LLM) serving infrastructure simulator (virtual testing software) developed by Professor Jongse Park's research team in the School of Computing won the Best Paper Award at ISPASS 2026 (IEEE International Symposium on Performance Analysis of Systems and Software).
"LLMServingSim 2.0," developed by the research team, is a simulation platform capable of virtually analyzing various hardware and software combinations in complex AI service environments. Researchers and developers can freely experiment with various design options and verify performance without having to directly build expensive, large-scale server infrastructures.
In particular, this technology is drawing attention because it goes beyond the existing Graphics Processing Unit (GPU)-centric environment to support diverse hardware environments, including Neural Processing Units (NPUs), which are rising as next-generation AI semiconductors, and Processing-In-Memory (PIM, a semiconductor technology that performs operations inside the memory).
