Building the Backbone of AI with Scalable and Power-Efficient Infrastructure

The AI revolution is being fuelled by continuous investment in infrastructure to handle vast and complex workloads. This includes moving towards distributed computing and using XPUS.

- Vinayak R. Adkoli

The exponential growth of artificial intelligence (AI) demands cutting-edge infrastructure capable of supporting increasingly complex workloads. This evolution in AI infrastructure hinges on scalability, energy efficiency, and openness, driven by advances in distributed computing, high-speed interconnects, and specialised hardware. Companies like Broadcom, with their specialised data centre chips and network technologies, are at the forefront of this transformation. Here's how these innovations are shaping the AI revolution.

Distributed computing: A network inside a network

AI workloads rely on distributed computing, where massive datasets are processed across interconnected systems. This paradigm functions as a “network inside a network,” where efficient communication between nodes is critical. Traditional infrastructures often struggle under the sheer scale and velocity of AI tasks.

Specialised hardware, such as Broadcom’s data centre chips, is redefining how systems communicate. These chips offer ultra-high-speed links that facilitate seamless data transfer between compute nodes, reducing latency and boosting throughput. By creating high-bandwidth, low-latency networks, distributed computing becomes far more efficient, supporting the complex, iterative training processes that drive AI development.

XPUs: Accelerating AI infrastructure