#USA #New York #ABI Research #Neoclouds #GPU-as-a-Service

Introduction

According to a recent forecast by ABI Research, neocloud providers are set to generate over $65 billion in revenue from GPU-as-a-Service (GPUaaS) by the year 2030. This rise is largely attributed to the escalating demand for AI-optimized infrastructure, which traditional hyperscalers have struggled to provide swiftly. Neoclouds are emerging as a specialized segment of cloud providers that cater specifically to the necessities of AI-first enterprises, carving out a substantial market share through scalable and developer-focused GPU compute environments.

Neoclouds: A Response to Evolving Needs

"Neoclouds go beyond serving merely as a stopgap for GPU shortages. They signify a fundamental transformation towards more customized and agile infrastructures designed for AI operations," explains Leo Gergs, Principal Analyst at ABI Research. The new paradigm allows enterprises to harness GPU capabilities tailored to their specific needs, a demand that traditional service providers have not fully addressed.

The distinction between neoclouds and older hyperscaler models is significant. Neocloud providers are not constrained by legacy systems. Instead, they craft greenfield environments that are fundamentally designed for AI performance, thereby offering better functionality and efficiency. This strategic approach is crucial for enterprises that require robust support for AI tasks, like foundation model training and multimodal inference.

Impact on the GPU Ecosystem

As the neocloud ecosystem evolves, ABI Research categorizes it into four major archetypes: GPUaaS opportunists, full-stack AI-first platforms, decentralized compute marketplaces, and domain-specific AI infrastructure providers. Leading players like CoreWeave, Lambda Labs, Crusoe Cloud, and Nebius are already pioneering changes in market strategies, employing models such as reserved capacity, bulk credits, and sovereign cloud deployments, along with MLOps integration for enhanced service delivery.

Gergs also warns that, while neoclouds offer cutting-edge solutions, they face the challenge of high capital intensity. Establishing large fleets of GPUs, developing orchestration layers, and providing sovereign infrastructures demand significant upfront investments. Unlike hyperscalers that benefit from additional streams of revenue such as adtech and SaaS, neoclouds must rely on strategic partnerships with chipset vendors to remain competitive. However, this reliance may turn these providers into new gatekeepers for access to AI infrastructures, much like traditional hyperscalers.

Evolving Pricing Models and Sovereignty

The implications of the rise of neoclouds extend beyond infrastructure capabilities. As these new providers innovate and diversify pricing models, sovereignty becomes a key differentiating factor in the marketplace. This shift is likely to lead to a substantial rebalancing of power within the AI ecosystem. It compels chipset makers, integrators, and enterprises to cultivate open ecosystems that prioritize operational transparency, thereby enhancing overall collaboration and preventing market lock-in.

Conclusion

In summary, the emergence of neoclouds represents a pivotal shift in the AI infrastructure value chain. By 2030, the demand for GPUaaS is expected to exceed $65 billion, reshaping how enterprises access and utilize GPU resources. Stakeholders across the board must adapt to this changing landscape by embracing transparency, fostering diverse partnerships, and remaining vigilant against potential market disruptions. As technology continues to evolve, so too must the strategies that underpin its implementation and sustainability.

For further insights and detailed analysis, ABI Research provides comprehensive studies on the evolving neocloud market landscape, featuring technology characteristics and business models pertinent to this emergent sector. For more information about ABI Research's services, clients can get in touch through their official channels.