According to DCD, Akamai Technologies has launched an inference cloud platform featuring Nvidia RTX PRO Servers with Blackwell GPUs, BlueField-3 DPUs, and Nvidia AI Enterprise software. The platform leverages Akamai’s distributed cloud infrastructure across more than 4,200 global edge locations, with initial deployment targeting 20 sites and plans for broader expansion. CEO Dr. Tom Leighton emphasized that putting AI decision-making closer to users mirrors the approach that enabled internet scaling, while Nvidia CEO Jensen Huang noted inference has become “the most compute-intensive phase of AI.” This expansion builds on Akamai’s growing cloud business, which generated $71 million in quarterly revenue ending August 2025, representing 30% year-over-year growth. This strategic move represents a significant evolution in edge computing infrastructure.
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Table of Contents
The Edge Inference Revolution
The significance of Akamai’s move extends far beyond simply adding another cloud service. We’re witnessing the maturation of a fundamental architectural shift in AI deployment. While training massive models requires centralized, powerful compute clusters, inference – the actual use of AI models – benefits tremendously from proximity to end users. This is particularly crucial for latency-sensitive applications like real-time translation, autonomous systems, and interactive AI assistants. Akamai’s existing edge infrastructure, originally built for content delivery, provides a ready-made distribution network that would take competitors years and billions to replicate.
Strategic Partnership Dynamics
The Nvidia partnership represents a symbiotic relationship that benefits both companies significantly. For Nvidia, it provides another massive distribution channel for their Blackwell architecture beyond traditional cloud providers. For Akamai, it offers immediate credibility in the AI infrastructure space without having to develop their own silicon. The inclusion of BlueField-3 DPUs is particularly noteworthy – these data processing units handle networking and security tasks, freeing up the GPUs to focus exclusively on AI workloads. This hardware-software integration creates a compelling alternative to the major hyperscalers, potentially appealing to enterprises seeking to avoid vendor lock-in.
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Market Implications and Competitive Landscape
Akamai’s entry creates a new competitive dynamic in the cloud computing AI space. While AWS, Google Cloud, and Microsoft Azure dominate AI training and centralized inference, Akamai’s edge-focused approach targets a different use case entirely. The company’s 30% cloud revenue growth indicates they’re successfully diversifying beyond their traditional CDN business. However, the challenge will be convincing enterprises to adopt a distributed inference strategy when most AI workloads currently run in centralized cloud environments. The success of this initiative will depend on whether application developers redesign their AI systems to leverage edge inference capabilities.
Technical and Operational Challenges
Distributing inference across thousands of locations introduces significant operational complexity. Model synchronization, version control, and consistent performance monitoring become exponentially more challenging when dealing with a globally distributed footprint. The Nvidia RTX hardware provides the computational foundation, but Akamai must develop sophisticated orchestration and management layers to make this practical for enterprise customers. Additionally, while edge locations reduce latency, they may struggle with the largest foundation models, creating a natural segmentation where smaller, specialized models run at the edge while massive models remain centralized.
Future Outlook and Industry Impact
This announcement signals the beginning of a broader industry trend toward specialized inference infrastructure. As AI becomes more pervasive, we’ll likely see more companies leveraging their existing distributed assets for AI workloads. The initial 20-location deployment is just the starting point – if successful, we can expect rapid expansion across Akamai’s entire edge network. This could fundamentally change how enterprises think about AI deployment, moving from a cloud-centric model to a hybrid approach where inference happens wherever it makes the most sense for latency, cost, and data sovereignty requirements.
