{"id":20069,"date":"2026-09-30T12:12:16","date_gmt":"2026-09-30T11:12:16","guid":{"rendered":"https:\/\/www.intelligentcio.com\/latam\/?p=20069"},"modified":"2026-09-30T12:12:16","modified_gmt":"2026-09-30T11:12:16","slug":"latam-mobile-networks-could-become-smarter-with-ai-ran-and-gpu-acceleration","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/latam\/2026\/09\/30\/latam-mobile-networks-could-become-smarter-with-ai-ran-and-gpu-acceleration\/","title":{"rendered":"LATAM mobile networks could become smarter with AI-RAN and GPU acceleration"},"content":{"rendered":"\n

Latin America\u2019s mobile networks face a double challenge: soaring data demand and pressure to make infrastructure more efficient. New 5G Open RAN trials from GlobalLogic suggest that combining AI with GPU acceleration could dramatically speed network processing, cut energy use and open the door to a more intelligent generation of mobile infrastructure.<\/em><\/p>\n\n\n\n

The next mobile revolution may be happening inside the network.<\/p>\n\n\n\n

The smartphone is only half the story.<\/p>\n\n\n\n

Behind every video stream, cloud application, industrial sensor and AI-powered service sits an increasingly complex web of radio equipment and computing infrastructure. As Latin America becomes more dependent on digital services, the pressure on that infrastructure is mounting.<\/p>\n\n\n\n

The question for operators is no longer simply how to build bigger networks. It is how to make those networks more intelligent, responsive and efficient.<\/p>\n\n\n\n

That is the promise of AI-RAN: integrating artificial intelligence into the radio access network so that computing and network resources can be optimised in response to changing conditions.<\/p>\n\n\n\n

GlobalLogic, the digital engineering company owned by the Hitachi Group, has been testing what happens when AI and GPU acceleration are brought into a 5G Open RAN environment. Its proof-of-concept trials produced striking results: processing times improved by as much as 15 times under certain conditions, while power consumption fell by around 40%.<\/p>\n\n\n\n

A second experiment produced an even more dramatic result. Using deep-learning models to distinguish genuine network-access attempts from those caused by noise or interference, GlobalLogic reports a 99.98% reduction in false detections.<\/p>\n\n\n\n

The figures come from laboratory tests rather than commercial deployments, so they should not be interpreted as guaranteed gains for operators. But they point towards a potentially significant shift in how mobile networks are designed and operated.<\/p>\n\n\n\n

According to the GSMA, global mobile data traffic could increase almost fivefold by 2030. More devices, richer applications, cloud services, video and AI workloads will all place additional demands on radio access networks – the infrastructure connecting users and devices to the wider network.<\/p>\n\n\n\n

For Latin America, that challenge intersects with another reality: operators need to expand and improve connectivity while controlling the cost and energy requirements of increasingly sophisticated infrastructure.<\/p>\n\n\n\n

Countries such as Colombia are already examining what the next generation of mobile networks will require. A study by Colombia’s Communications Regulation Commission (CRC) highlights spectral efficiency, energy efficiency, distributed processing and artificial intelligence as important elements in the evolution of mobile networks.<\/p>\n\n\n\n

Traditional RAN architectures were designed around relatively predictable workloads, with resources allocated according to established configurations. That model becomes harder to sustain as traffic patterns become more volatile.<\/p>\n\n\n\n

A stadium can suddenly generate a huge spike in demand. An industrial site can produce thousands of machine connections. A cloud application can shift workloads rapidly. An AI service can create new requirements for low-latency connectivity and computing.<\/p>\n\n\n\n

AI can help operators respond to those changes dynamically, potentially improving spectrum utilisation, energy efficiency and network performance.<\/p>\n\n\n\n

\u201cAmong its possibilities are a more efficient use of the spectrum, a reduction in energy consumption, better management of network resources and the ability to respond dynamically to changes in traffic,\u201d says Sebasti\u00e1n Bainer, Senior Vice President and Head of Latin America, GlobalLogic.<\/p>\n\n\n\n

For operators, the attraction is therefore not AI for its own sake, but the prospect of extracting more capacity and efficiency from increasingly complex infrastructure.<\/p>\n\n\n\n

AI workloads are particularly suited to GPUs because they can perform large numbers of calculations in parallel. That makes accelerated computing attractive not only for AI applications but also for telecommunications workloads requiring substantial processing power.<\/p>\n\n\n\n

The reported 15x improvement in processing times under certain conditions illustrates what accelerated computing could bring to demanding network workloads. The approximately 40% reduction in power consumption is equally important.<\/p>\n\n\n\n

Operators have traditionally added capacity as traffic grows. If more processing can be completed quickly and efficiently, there is potentially another route: make the underlying infrastructure smarter rather than simply bigger.<\/p>\n\n\n\n

Wireless networks are inherently noisy environments. Interference and signal distortion can lead systems to identify events that do not represent genuine network-access attempts.<\/p>\n\n\n\n

GlobalLogic used deep-learning models to address the problem and reports a 99.98% reduction in false detections during its test.<\/p>\n\n\n\n

The broader lesson is that AI does not necessarily have to mean more computation. Used effectively, it can also mean less wasted computation.<\/p>\n\n\n\n

AI-RAN’s development is closely linked to Open RAN.<\/p>\n\n\n\n

By separating network functions and encouraging greater interoperability between hardware and software components, Open RAN creates an environment in which software-defined capabilities and alternative computing architectures can play a larger role.<\/p>\n\n\n\n

That matters because AI-RAN is unlikely to be a single piece of equipment that operators simply install. It is better understood as an architectural shift bringing together radio systems, software, AI models, accelerated computing and distributed processing.<\/p>\n\n\n\n

For Latin American operators, that could eventually provide a route to introducing new capabilities incrementally rather than treating the transition to future networks as one massive upgrade.<\/p>\n\n\n\n

GlobalLogic’s experiments do not prove that AI-RAN is ready to transform commercial mobile networks overnight. They demonstrate, in a controlled 5G Open RAN environment, that the underlying technologies can produce measurable gains.<\/p>\n\n\n\n

The region’s next connectivity challenge will not simply be about adding more towers, spectrum or capacity. It will be about extracting more intelligence and efficiency from the infrastructure being built today.<\/p>\n\n\n\n

If the mobile network of the future can dynamically decide where to compute, where to save energy and what to ignore, the next revolution in connectivity may be less visible than the last one.<\/p>\n\n\n\n

It may happen inside the network itself.<\/p>\n","protected":false},"excerpt":{"rendered":"

Latin America\u2019s mobile networks face a double challenge: soaring data demand and pressure to make infrastructure more efficient. New 5G Open RAN trials from GlobalLogic suggest that combining AI with GPU acceleration could dramatically speed network processing, cut energy use and open the door to a more intelligent generation of mobile infrastructure. The next mobile […]<\/p>\n","protected":false},"author":58,"featured_media":20070,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5219,4674,3055,3],"tags":[161,6688,112,6277,6729,288,1222,8231,242],"class_list":["post-20069","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-5g","category-ai","category-telecom","category-top-stories","tag-5g","tag-ai-ran","tag-artificial-intelligence","tag-globallogic","tag-gpu-acceleration","tag-latin-america","tag-mobile-networks","tag-open-ran","tag-telecommunications"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/posts\/20069","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/comments?post=20069"}],"version-history":[{"count":1,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/posts\/20069\/revisions"}],"predecessor-version":[{"id":20071,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/posts\/20069\/revisions\/20071"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/media\/20070"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/media?parent=20069"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/categories?post=20069"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/latam\/wp-json\/wp\/v2\/tags?post=20069"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}