AI workloads are pushing thermal limits in ways traditional system designs were not built to handle. In this webinar, Fujipoly explores how thermal interface materials are becoming a critical factor in AI system performance, reliability, and scalability.
Overview
The rapid growth of AI and accelerated computing is driving unprecedented power densities, higher heat flux, and tighter mechanical constraints inside modern systems. As CPUs, GPUs, and accelerators continue to scale, thermal interfaces are no longer an afterthought. They are a key enabler of performance and reliability.
In this interview, Fujipoly examines how demand from AI applications is changing expectations for thermal interface materials. We will discuss where thermal resistance actually occurs in AI systems, what performance characteristics matter most for TIMs under high heat flux, and why not all AI platforms can rely on the same thermal interface solutions. Attendees will gain insight into when engineers should be thinking about TIM selection during the design process, how liquid cooling and advanced heat sinks are influencing interface requirements, and whether certain legacy TIM approaches are becoming obsolete.
We will also highlight specific thermal interface material categories, including high performance gap fillers and form in place solutions, that are being used to address the unique challenges of AI hardware. Finally, we will outline how Fujipoly partners with engineers early in the design cycle to support effective and scalable thermal management strategies.
Key Takeaways
- Recognize how AI workloads are shifting thermal bottlenecks from heat sinks to interfaces
- Evaluate TIM performance based on total thermal resistance, compliance, and long term reliability, not conductivity alone
- Apply the right thermal interface strategy early in the design process to avoid late stage thermal and mechanical failures
