Broadcom has introduced new AI-powered advancements to its VeloCloud portfolio, including the VeloRAIN architecture, high-performance Edge appliances, and the Titan partner program, aimed at accelerating enterprise readiness for AI and edge applications. These innovations offer improved connectivity, security, and scalability to meet the demands of distributed enterprise networks.
7 November 2024 – Broadcom Inc. has announced a series of key enhancements to its VeloCloud portfolio, focusing on boosting enterprise readiness for both AI and non-AI workloads at VMware Explore 2024 in Barcelona. The newly introduced VeloRAIN (Robust AI Networking) architecture employs AI and ML technologies to optimize performance and security across distributed AI networks, addressing enterprise demands for scalable, high-performance connectivity.
The company also launched the VeloCloud Edge 4100 and 5100 appliances, AI-ready edge solutions supporting up to 100Gbps, designed to handle complex enterprise environments requiring high-speed, robust connectivity. With these solutions, Broadcom is expanding its support for large data centers and branch offices, enabling streamlined scalability and lower latency. The appliances deliver advanced SD-WAN, security, and AI capabilities to simplify network infrastructure while maximizing resilience for growing data workloads.
Furthering its commitment to partner success, Broadcom unveiled Titan, a new Advantage Partner Program for VeloCloud Managed Service Providers (MSPs). The program offers performance-based rewards, exclusive access to Broadcom’s latest technologies, and flexible licensing models to empower MSPs to enhance service delivery in AI-driven environments. Titan’s tiered structure supports partner growth with unique benefits, including white-label opportunities for top partners to diversify customer bases.
Broadcom also released the “State of the Edge” report, underscoring the growth of AI workloads at the edge. The report reveals that edge solutions adoption is driven by connectivity issues (57%) and faster response time needs for latency-sensitive applications (68%). The findings emphasize that edge AI deployments lead to smarter, quicker decision-making, supporting enterprises in processing data efficiently and enhancing productivity.
