China’s answer to AI’s growing appetite for computing power may involve moving data with light instead of adding more GPUs.
Peking University researchers developed an optical interconnect system that reportedly made distributed AI inference more than 100 times faster while using one-ninth of the usual computing resources, according to the South China Morning Post.
The early-stage research could give China and the wider APAC region another route to faster, more energy-efficient AI infrastructure as data center operators confront rising power demands, hardware costs, and processor supply constraints.
Optical links tackle a growing AI bottleneck
SCMP noted that the researchers connected standard electronic chips using custom optical hardware and algorithms.
The system used field-programmable gate arrays (FPGAs), programmable chips commonly used in data centers, autonomous vehicles, and other applications that require high levels of parallel processing.
A silicon photonic transceiver handled the conversion between electrical and optical signals at 400 gigabits per second. A second component managed communication among the chips, allowing data to travel over optical links rather than relying solely on slower electrical connections.
Inside AI reported that the system maintained 99.5% of the accuracy achieved by a single-chip setup while processing data at about 100 times the rate of a comparable electrical system.
The approach targets one of AI infrastructure’s most stubborn problems. As models grow larger, processors spend more time and energy transferring data among chips. Adding GPUs can increase capacity, but it also raises power use, cooling requirements, and equipment costs.
China looks for another way to scale AI
The research is particularly relevant to China, as its technology companies and data center operators seek additional computing capacity without relying solely on larger GPU clusters.
Optical interconnects could eventually help Chinese AI developers move data between processors faster while reducing the amount of computing hardware required for inference. Greater efficiency would be valuable as operators manage electricity use, cooling demands, equipment costs, and access to advanced processors.
Compatibility with commonly used FPGAs also gives the design a practical advantage over optical computing systems built around entirely custom processors. Existing data centers would still need significant hardware changes before they could use the technology at scale.
The breakthrough is not ready for data centers
The system remains a laboratory demonstration rather than a production-ready platform.
Researchers must still miniaturize and package the optical components, test their reliability over long periods, and prove that the performance gains hold across much larger clusters. Silicon photonic hardware may also be more difficult and expensive to manufacture than conventional electrical connections.
Inside AI reported that the system still needs to prove it can maintain the same performance across much larger clusters with thousands of connected nodes.
Chinese cloud providers and enterprises should view the findings as a possible direction for future infrastructure, rather than as equipment they can purchase today.
The study suggests China could increase AI inference capacity through more efficient chip-to-chip communication, but commercial deployment will depend on whether the design can preserve its latency, accuracy, and resource savings at scale.
Read more: China’s DeepSeek is reportedly developing its own AI chip to reduce reliance on Nvidia and Huawei.