As the world of artificial intelligence (AI) accelerates from the development of training models to inference, Akamai Technologies claims to have reached a major milestone in the evolution of AI, unveiling the first global-scale implementation of Nvidia AI Grid reference design.
Putting the launch into context, Akamai observed that the first wave of AI infrastructure was defined by large graphics processing unit (GPU) clusters in a handful of centralised locations, optimised for training. But, it stressed, as inference becomes the dominant workload and businesses across every industry focus on building AI agents, that centralised model faces the same scaling constraints that earlier generations of internet infrastructure encountered with media delivery, online gaming, financial transactions and complex microservices applications.
Akamai claims it is solving each of those challenges through the same fundamental approach – distributed networking, intelligent orchestration and purpose-built systems that bring content and context together as close as possible to the digital touchpoint.
The result is said to be improved user experiences and stronger return on investment for enterprises that adopt the model, along with the ability to deploy AI agents that are context-aware and adaptive in their responsiveness. Akamai believes it represents a blueprint for how AI factories evolve from isolated installations into a globally distributed utility.
The deal with Nvidia marks a significant step in the evolution of Akamai’s Inference Cloud, which was introduced in October 2025 and is designed to apply this architecture to AI factories and support the next wave of scaling and growth by distributing dense compute from core to edge.
By integrating Nvidia AI infrastructure into Akamai’s infrastructure and leveraging intelligent workload orchestration across its network, Akamai intends to move the industry beyond isolated AI factories towards a unified, distributed grid for AI inference.
At the heart of the AI Grid is an intelligent orchestrator that acts as a real-time broker for AI requests. By applying application performance optimisation to AI, the workload-aware control plane is said to be able to optimise “tokenomics” by radically improving cost per token, time to first token and throughput.
In the new world of tokenomics, Akamai believes enterprises can “dramatically” reduce inference costs by matching workloads to the right compute tier automatically. The orchestrator applies techniques such as semantic caching and intelligent routing to direct requests to right-sized resources, reserving premium GPU cycles for the workloads that demand them. Underpinning this is the Akamai Cloud infrastructure to support data-intensive AI operations at scale.
“AI factories have been purpose-built for training and frontier model workloads – and centralised infrastructure will continue to deliver the best tokenomics for those use cases,” said Adam Karon, chief operating officer and general manager for Akamai’s cloud technology group.
“But real-time video, physical AI and highly concurrent personalised experiences demand inference at the point of contact, not a round trip to a centralised cluster. Our AI Grid intelligent orchestration gives AI factories a way to scale inference outward – leveraging the same distributed architecture that revolutionised content delivery to route AI workloads across 4,400 locations, at the right cost, at the right time.”
Looking deeper at use cases, Akamai said financial institutions could execute personalised fraud detection and marketing recommendations in the moment between login and first screen, broadcasters could transcode and dub content in real time for global audiences, and retailers could adopt the network for in-store AI applications and associate productivity tools at the point of sale.
These outcomes are built on Akamai’s globally distributed edge network, with over 4,400 locations, integrated caching and serverless edge compute, and that processes requests at the point of user contact, bypassing the round-trip lag of origin-dependent clouds.
As the first to operationalise the AI Grid, Akamai is rolling out thousands of Nvidia RTX PRO 6000 Blackwell Server Edition GPUs, providing a platform to enable enterprises to run agentic and physical AI with the responsiveness of local compute and the scale of the global web. The GPU clusters, powered by the Blackwell Server Edition GPUs, provide the concentrated horsepower for the heaviest AI workloads, complementing the distributed edge with centralised scale.
“New AI-native applications demand predictable latency and better cost efficiency at planetary scale,” remarked Chris Penrose, global vice-president of telco business development at Nvidia. “By operationalising the Nvidia AI Grid, Akamai is building the connective tissue for generative, agentic and physical AI, moving intelligence directly to the data to unlock the next wave of real-time applications.”
