IBM researchers have demonstrated a way to bring the speed and capacity of optical components to data centers.

In a technical article, IBM introduced a new prototype co-packaged optics (CPO) module that can enable high-speed optical connectivity. Researchers have developed a new method for CPO to enable connectivity within data centers at the speed of light, complementing existing short-range electrical lines. This technology could significantly increase data center communications bandwidth, minimizing GPU downtime while dramatically accelerating AI processing. This research innovation, as described, could significantly improve the way data centers train and run generative AI models, IBM said.

By designing and assembling the first publicly announced successful polymer fiber optic cable (PWG) to power this technology, IBM researchers demonstrated how CPO will redefine the way the computer industry transfers high-bandwidth data between chips, circuit boards and servers .

Nowadays, fiber optic technology transmits data at high speeds over long distances for commerce and communication. Data centers use fiber optics for external communications networks, but racks within these data centers still operate communications primarily over copper-based electrical lines. These lines connect GPU accelerators, which may spend more than half of their time idle, waiting for data from other devices in a large, distributed training process, which can incur significant costs and energy, IBM said.

According to IBM, this new approach could result in lower costs for scaling generative AI by reducing energy consumption by more than five times compared to mid-range electrical connections, while increasing the length of data center interconnection cables from one to hundreds of meters.

Additionally, it could speed up training of AI models, allowing developers to train a large language model (LLM) up to five times faster – reducing the time needed to train a standard LLM from three months to three Weeks are shortened and the performance increase increases through the use of larger models and more GPUs. And it could dramatically increase the energy efficiency of data centers, saving the energy equivalent of the annual electricity consumption of 5,000 U.S. homes per trained AI model, according to IBM researchers.

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