HPE and Juniper Networks launched cloud-native routing hardware and AI-optimized platforms at Mobile World Congress 2026 in Barcelona, addressing infrastructure demands from telecommunications operators worldwide adapting networks for artificial intelligence workloads.
"AI infrastructure is a critical growth driver for service providers," said Rami Rahim, HPE executive. "HPE is committed to helping these customers lead in the AI era by building intelligent, next-generation networks that can support complex operations, rising data traffic, and the transformative capabilities of AI."
The products combine high-performance routing with AI-native automation for telco cloud architectures. Service providers from North America to Asia face mounting pressure to upgrade networks as AI applications proliferate across enterprise customers and consumer services.
"HPE delivers the architectural and operational foundation service providers need to fully participate in the AI value chain," said Ray Mota, industry analyst. The infrastructure enables secure, autonomous digital services at scale across international markets.
AI workloads are reshaping global traffic patterns and creating demands for uplink capacity, latency reduction, and network throughput. Distributed AI training operations spanning multiple data centers require bandwidth exceeding traditional network architectures' capabilities.
The announcements accompany broader industry shifts in GPU virtualization, power delivery systems, and financing models designed to accelerate AI adoption. European, Asian, and North American operators compete to offer differentiated AI services to multinational enterprise clients.
Cloud-native router designs enable faster deployment and flexible scaling compared to legacy hardware. High-density platforms reduce data center space requirements, particularly valuable in land-constrained markets like Singapore, Hong Kong, and major European cities.
The convergence of AI optimization across networking, compute, and power domains reflects infrastructure providers' recognition that AI workloads require holistic system design. Bottlenecks in any component limit performance across distributed international networks.
Built-in security features address growing regulatory concerns about AI system vulnerabilities and data protection requirements varying across jurisdictions from GDPR in Europe to emerging frameworks in Asia-Pacific markets.