Below, we’ll explore where Traditional IT and AI networking differ. These are key shifts needed to embrace the AI era: speed, latency, and predictability.

• Predictability: Traditional networks struggle with the dynamic nature of AI workloads, leading to performance fluctuations. Sometimes, the demand can be high; other times, it can be CPU-burning intensive. AI networking demands
  a system that anticipates and adapts to changes in workload. This ensures predictability, which is crucial for the stability and reliability of AI applications (without burning the motherboards).
• Speed: Yes, traditional IT networks prioritise fast data transfer. AI applications are much more. They need instantaneous responses. This means significantly reducing the time it takes for data to travel. Speeds like 400 or 800 Gbps (and 1.6 Tbps in the near future) are pretty common in an AI infrastructure for communication between the GPUs and the storage systems. Think of it like the difference between a lightning strike and the slow rumble of distant thunder).
• Latency: Traditional networks often introduce unpredictable delays. Too many people on the server, working on a demanding app, or even weather conditions sometimes play a role. This simply can’t happen with AI. AI needs a network that guarantees consistent, split-second responses even when the workload is demanding. AI networks vs. IT are the difference between a smooth, lag-free video call and one constantly buffering.

For AI applications to function smoothly, they require immediate and predictable data access. That means it’s time to rethink storage principles and enact changes that fully embrace the era of AI.

InfiniBand is a high-speed solution built explicitly for AI’s unique needs. Compared to the widely used Ethernet, InfiniBand boasts unrivalled speed and low latency. This matters because AI applications constantly exchange massive amounts of data,
and any delays can impact performance. Choosing the right network, whether InfiniBand or Ethernet, hinges on understanding these significant speed disparities to ensure you build an AI-ready network.

These serve as the blueprint for architects and engineers to design AI-ready networks. Networks capable of handling the dynamic and demanding nature of modern AI applications. The synergy between speed, low latency, and predictability
is the backbone of AI networking, shaping the future of technology-driven interactions.

Beyond speed, there is another evolution in AI networking: The Intelligent network. An Intelligent network can autonomously find the best routes for data to travel. Smart Network Interface Cards (NICs) enable this by being embedded within servers. These cards offload tasks, transforming the network from passive bystanders to active contributors within the AI ecosystem. Compare that to the traditional IT network where CPUs queue to receive the tasks. Apart from that,, the Smart NICs can also operate while simultaneously bypassing the CPU. The smart NIC itself (RDMA) transfers data directly to another server memory. This accelerates data transfer and
frees the CPU for other tasks. Smart Networks: Embracing Intelligence AI networking isn’t just about speed; it’s about intelligence. This means the network can automatically find the best routes for data to travel, like a supercharged GPS for information packets. This leap towards intelligent networks is powered by Smart
Network Interface Cards (NICs) embedded within servers.