In a rapidly evolving AI landscape, Verizon is making strategic moves to leverage distributed infrastructure for AI inferencing. As the reliance on AI grows, large-scale investments are being made in AI infrastructure. Notably, OpenAI and partners have announced a massive $500 billion investment, while Chinese startup DeepSeek claims to rival OpenAI’s technology with their own.
Verizon has introduced the AI Connect suite, aiming to facilitate large-scale AI deployments for businesses. This approach focuses on reducing latency, enhancing data privacy, and improving cost-efficiency of AI processes. Traditionally, AI inferencing relies on large, centralized cloud facilities. Yet, the trend shows a growing interest in edge AI for its effectiveness in reducing delays and cost by processing data closer to the source – your personal devices or local network infrastructure.
The company highlights a significant forecast by McKinsey. By 2030, 60% to 70% of AI workloads will involve real-time inference. This trend underscores the need for low-latency connectivity and robust infrastructure at the network edge. Verizon’s extensive network, equipped with fiber, compute, space, power, and cooling capabilities, positions it as a leader in supporting edge AI demands. It is already providing capacity to Google Cloud and Meta.
Collaboration plays a key role in Verizon’s strategy, with partners including NVIDIA, Google, Meta, and Vultr, a GPU as a service (GPUaaS) and cloud computing provider. These partnerships will help bolster their capability to handle existing and evolving AI workloads.
Verizon Business CEO Kyle Malady expressed confidence in the company’s leadership in network infrastructure. “We are seeing significant demand for reliable network infrastructure that can support existing AI workloads,” he said. Their edge-to-cloud connectivity and programmable network are expected to push the boundaries of innovation in AI inferencing.
