Why Did Akamai Acquire Thousands of NVIDIA Blackwell GPUs?

Among enterprises making big AI moves is Akamai, which has announced the acquisition of thousands of NVIDIA Blackwell GPUs. 

Akamai is set to roll these out across its global cloud network as part of a major push to build one of the most widely distributed AI platforms in the world.

The expansion will see Akamai Technologies integrate next generation NVIDIA Blackwell GPUs into its global footprint, which exceeds 4,000 edge locations.

Rather than concentrating compute power in a handful of hyperscale data centres, the company is embedding AI inference capacity deep within its distributed cloud platform.

The focus is squarely on inference, the stage where trained models are put to work in real world applications. 

More than half of the organisations cited latency as the primary barrier to scale AI, according to a report by MIT Technology Review.

By positioning high performance GPUs closer to end users and connected devices, Akamai aims to reduce latency by up to 2.5 times, lower bandwidth costs and allow organisations to process and act on data where it is created.

The deployment is designed to support use cases that rely on immediate responses, including autonomous machines, digital health systems, industrial automation and financial fraud detection.

In these environments, even small delays can limit effectiveness, making proximity of compute a competitive advantage.

“While hyperscalers continue to push the boundaries of AI training, Akamai is focused on meeting the unique demands of the inference era,” says Adam Karon, Chief Operating Officer and General Manager of Cloud Technology Group at Akamai.

“Centralised AI factories remain essential for building models, but bringing those models to life at scale requires a decentralised nervous system. 

“By distributing inference-optimised compute across our global fabric, Akamai isn’t just adding capacity.

“We’re providing the scale, at minimal latency, that is required to move AI from the laboratory to the street corner and the hospital bed – where the work happens, where the data lives and where the ROI is realised.”

Edge AI platform strategy targets low latency and lower costs

Akamai’s approach reflects a broader shift in the AI market.

While hyperscale providers continue to invest heavily in massive training clusters, enterprises are increasingly concerned with how to run models efficiently in production.

By embedding Nvidia Blackwell GPUs directly into its distributed cloud infrastructure, Akamai is positioning itself as an alternative to centralised AI factories and can save business up to 86% on AI inference compared to traditional hyperscale infrastructure. 

The Blackwell architecture is designed to deliver high throughput and improved energy efficiency for demanding AI inference workloads, making it well suited to geographically dispersed deployments.

The company says its architecture dynamically routes workloads to optimal GPU clusters within its network.

This enables businesses to fine tune and deploy large language models closer to regional users, which can help address data sovereignty and compliance requirements alongside performance objectives.

For developers, the platform is intended to provide scalable GPU access without the complexity of managing infrastructure across multiple regions.

For enterprises, the appeal lies in predictable performance and reduced data transfer costs when serving AI powered applications at scale.

Scaling AI inference beyond hyperscale data centres

The decision to deploy thousands of Nvidia Blackwell GPUs signals Akamai’s ambition to play a central role in the next phase of AI infrastructure.

Instead of competing directly on large scale model training, the company is betting that inference at the edge will define the commercial impact of AI.

As AI systems move from experimentation to embedded services in healthcare, manufacturing, retail and finance, the need for distributed, low latency compute is expected to intensify. 

By extending GPU acceleration across its global network, Akamai is seeking to ensure that AI models are not only powerful but also practical in everyday settings.

If successful, the rollout could help reshape how organisations think about deploying AI, shifting the emphasis from centralised capacity to globally distributed performance.