AI workloads are no longer simple compute tasks running on a single processor. Large Language Models move massive amounts of data across GPUs, CPUs, memory, storage, and high-speed interconnects. Every decision— from how workloads are split to which interconnect is used— can affect performance, power, heat, and cost.
This is why architecture planning has become one of the most important steps in building next-generation AI systems. Before hardware is finalized, teams need to answer practical questions: Where should the workload run? How much memory is enough? Which interconnect gives the best performance? When does adding more hardware stop helping? How do we reduce heat without slowing the system down?
That is where VisualSim Architect helps.
VisualSim allows teams to create a digital twin of an AI SoC or data center system before the real hardware exists. Instead of waiting until late-stage development to discover bottlenecks, engineers can model the system early, run different workload scenarios, and see how the architecture behaves under pressure.
For LLM workloads, this means teams can study how tasks move across accelerators, memory, and interconnects such as CXL memory, NVLink, PCIe, and UALink. They can compare different choices for selection, sizing, and partitioning, and understand the impact on latency, utilization, power, and thermal behavior.
The value is simple: make better architecture decisions earlier.
In the upcoming Mirabilis Design webinar, Kesudh Giri will walk through how VisualSim can be used to evaluate AI SoC interconnect trade-offs and improve workload performance. The session will show how digital twins help identify congestion points, reduce over-provisioning, improve hardware utilization, and support better power-performance decisions.
This is especially important as AI systems become more expensive and complex. A small design decision at the architecture stage can lead to major differences in performance, energy use, and deployment cost. VisualSim gives teams a way to test those decisions before committing to hardware.
The webinar will offer a practical preview of how simulation-driven design can help teams move from guesswork to measurable architecture planning.
Webinar Topic:
Optimizing LLM Workload Performance through Selection, Sizing and Partitioning across AI SoC Interconnects
Speaker: Kesudh Giri, Mirabilis Design
This session is designed for teams working on AI infrastructure, SoC architecture, accelerators, memory systems, and high-performance computing platforms who want to understand how early digital twin modeling can reduce risk and improve system performance.
To register click below:
Asia Session
1:00 PM IST | 4:30 PM JST/KST | 2:30 PM China | 9:30 AM CEST
Register here: Link
US / EMEA Session
10:00 AM PST | 1:00 PM EST | 5:00 PM BST
Register here: Link
