The demand for artificial intelligence (AI) is skyrocketing, creating significant pressures in the tech infrastructure landscape. Traditional cloud service giants such as Amazon Web Services, Microsoft Azure, and Google Cloud are feel the strain as the capacity for AI training models becomes increasingly limited. In response to this burgeoning demand, a new segment of providers known as “neoclouds” is emerging, positioning themselves as vital players in the future of AI development.

Neoclouds represent a shift in how AI computing resources are delivered. Unlike major cloud platforms that offer comprehensive software solutions, these smaller infrastructure firms specialize in leasing clusters of graphics processing units (GPUs) tailored for AI developers and enterprises. The business model revolves around providing rapid access to high-performance computing resources, crucial for enterprises that require immediate support for their AI initiatives.

The increasing complexity and size of modern AI models necessitate high levels of computing power, far exceeding what traditional data centers were initially designed to accommodate. This situation presents a challenge, as GPUs consume substantial amounts of electricity and generate considerable heat, requiring sophisticated cooling systems to maintain optimal performance. Indeed, most existing data centers are not optimized for the high-density workloads demanded by AI applications.

A recent analysis by KPMG highlights a striking trend: investment in GPUs and related hardware is currently growing at a rate approximately five times faster than that of new data-center construction. In further research, JLL indicates that neoclouds have a distinct advantage, capable of deploying high-density GPU infrastructures within months, a far cry from the lengthy multiyear build-out times associated with hyperscale data centers. The efficiency and speed at which neoclouds can mobilize resources present a significant opportunity for organizations in need of expedited AI development.

Neoclouds operate on a smaller scale, focusing exclusively on compute capabilities. This specialty allows them to set up rapidly and to configure high-density GPU clusters efficiently, providing flexible leasing arrangements that cater to the fluctuating requirements of their clients. Many neoclouds offer their services on an hourly or monthly basis, allowing AI startups, research institutions, and other businesses to respond quickly to their computing needs without long-term financial commitments.

This model serves as a valuable resource for companies that typically utilize major cloud providers for deployment but require temporary bursts of capacity for model training. The flexibility that neoclouds provide mirrors strategies employed in sectors such as logistics and energy, where short-term capacity contracts become critical in times of heightened demand.

The growth of the neocloud segment has been impressive, reflecting the heightened urgency for AI resources amidst scarcity. JLL’s data reveals that this segment has expanded at a compound annual growth rate of 82% since 2021, significantly surpassing the overall investment trends in the data center market.

The neocloud sector has already attracted major clients and investors, further validating its potential. CoreWeave secured a $22.4 billion contract with OpenAI to furnish dedicated GPU clusters, a telling indicator of the demand for specialized computing power. Additionally, Nebius recently raised $3.75 billion following a compute supply agreement with Microsoft, underscoring the strategic nature of these partnerships in advancing AI capabilities. Reports indicate that Nvidia has plans to invest up to $100 billion in data centers linked to OpenAI, signaling the importance of the compute landscape in supply planning for AI firms.

As AI continues to evolve, the role of neoclouds in providing critical computing resources will become increasingly important. By bridging the existing capacity gaps in traditional cloud offerings, these providers are not only supporting the acceleration of AI innovations but also redefining the infrastructure landscape to accommodate the future demands of artificial intelligence.