Why AI is Trending Local: Solving the Bandwidth Crisis for Image and Video Processing

DALL-E and SORA exemplify the growing challenge in AI where data processing must happen close to the user due to the sheer volume of data. Both applications—DALL-E’s high-resolution image generation and SORA’s real-time video analysis—produce massive amounts of data that cannot efficiently traverse traditional networks. Networks can’t handle large amounts of data without significant latency, congestion, bottlenecks, and cost. This constraint makes local computing essential.

The Bandwidth Challenge

For AI models like DALL-E, each high-resolution image generated can range from tens to hundreds of megabytes, multiplying rapidly when scaled across numerous requests. Transmitting these images to a centralized data center over fiber networks would overload available bandwidth, especially when demand spikes. Even a modest increase in usage could strain traditional routes to the point where latency becomes inevitable, hampering the real-time experience users expect.

The data burden is even more significant with video AI applications like SORA. Processing HD or 4K video at 30-60 frames per second generates terabits of data per second. Attempting to send this data back to a centralized facility for processing would require massive bandwidth that current networks are ill-equipped to handle, creating severe delays and interruptions. For real-time video analytics—such as for autonomous vehicles, live security monitoring or interactive streaming—any lag in processing could result in compromised functionality or, in critical applications, real-world consequences.

Why Local Compute Is the Solution

AI processing must happen locally to manage this data without overwhelming the network. By bringing compute resources closer to the user, applications like DALL-E and SORA can function without flooding traditional data pipelines. Localized processing means that the data generated by these applications doesn’t need to travel far, reducing latency and bypassing the bandwidth limitations of long-distance network routes.

For instance:

• DALL-E: Generating images near the user, within edge data centers or modular deployments, allows quick delivery without waiting for data to traverse congested networks. The high-bandwidth requirements are contained within a local network, avoiding the need for extensive data transfer over fiber.
• SORA: Real-time video analysis is best conducted on-site or nearby to process video feeds instantly and prevent bandwidth overload. Local computing eliminates the need to backhaul video data to centralized locations, enabling real-time insights and decisions without latency that could disrupt operations.

High-Speed Local Networks: Enabling Efficient Data Flow

High-speed local networks like 5G and Radio Access Networks (RAN) can efficiently handle data flow by situating processing infrastructure near end-users. These networks are built to support high-bandwidth, low-latency transmission within localized areas, which is ideal for supporting data-heavy applications like DALL-E and SORA. Edge computing infrastructure can tap into these local networks to process and deliver AI insights on the spot, significantly reducing the burden on long-distance data networks.

As AI applications grow in complexity and volume, the traditional centralized processing model must be augmented with a distributed model where processing happens closer to users, which will be essential. Edge computing, modular data centers, and high-speed local networks provide the infrastructure needed to meet the data demands of next-generation AI applications without overwhelming existing network routes.