Nvidia Corporation is reinforcing its position as a pivotal force in the global artificial intelligence (AI) infrastructure landscape. Central to this development is the companys GB300 platform, which is anticipated to become the core technology underpinning next-generation data centers on a global scale.
Industry analysts forecast that in 2026, servers equipped with Nvidias GB300 chips will comprise an estimated 70% to 80% of total AI server rack shipments worldwide. The platforms ascendance comes as shipments of GPU-based rack systems, including Nvidias Vera Rubin 200, and AMDs MI400 platform, are projected to sharply increase this year. The Vera Rubin 200, slated for broader adoption after the third quarter, represents a more powerful iteration within Nvidias Blackwell lineup, albeit with substantially higher power consumption demands.
TrendForce analyst Frank Kung noted that mass production of GB300-based servers commenced in the previous quarter, positioning them as the predominant models utilized by Taiwanese server manufacturers throughout 2026. This trend aligns with an overall industry shift to specialized AI infrastructure, incorporating custom ASIC solutions increasingly adopted by major cloud service providers such as Google (Alphabet Inc.), Amazon Web Services (AWS), and Meta Platforms.
The growing power density inherent to these advanced AI systems is fueling a parallel surge in demand for liquid cooling solutions. Fiona Chiu, also from TrendForce, highlighted that the persistent expansion of AI data centers necessitates innovative cooling approaches to effectively manage rising thermal loads, underpinning the operational viability of these high-performance technologies.
Large-scale deployments are already reinforcing the GB300 platforms central role. Nscale, a notable AI infrastructure developer, has expanded its partnership with Microsoft to deploy a significant AI server ecosystem featuring approximately 200,000 GB300 GPUs across sites in the United States and Europe.
A flagship U.S. facility located in Texas plans to integrate about 104,000 GB300 GPUs within a 240-megawatt (MW) AI campus. Services for Microsoft clients at this site are projected to commence by the third quarter of 2026, with plans to scale the campus to a 1.2-gigawatt (GW) capacity in the longer term. Additionally, Microsoft holds an option to implement a second phase of the project, encompassing an estimated 700 MW, starting in late 2027.
On the European front, Nscale intends to install GB300 systems across several countries: around 12,600 GPUs in Portugal beginning early 2026, approximately 23,000 units at a U.K. campus set for 2027, and roughly 52,000 GPUs in Norway.
In a parallel development, HUMAIN, a company backed by Saudi Arabia's Public Investment Fund, has broadened its collaboration with Nvidia to establish sovereign AI infrastructure both in Saudi Arabia and the United States. The plan involves deploying up to 600,000 Nvidia AI systems over the next three years, with the GB300 platform being a core component of this rollout.
This surge in deployment and infrastructure investment underscores Nvidias expanding footprint in the AI hardware space, particularly as AI workloads demand increasingly specialized and powerful computing resources. At the same time, Nvidia shares traded down 2.45%, closing at $181.66 during recent premarket activity.
Key Points:
- The Nvidia GB300 platform is projected to dominate the global AI server market in 2026, accounting for an estimated 70% to 80% of AI server rack shipments.
- Mass production of GB300-based servers has commenced, with Taiwanese server manufacturers adopting these as core models for 2026.
- Strategic partnerships are in place for large-scale deployments, including Nscales projects with Microsoft across the U.S. and Europe, and HUMAINs sovereign AI infrastructure initiative involving Saudi Arabia and the U.S.
- Rising power densities in AI data centers are driving increased demand for liquid cooling solutions to support efficient thermal management and system reliability.
Risks and Uncertainties:
- The elevated power consumption of next-generation platforms like Vera Rubin 200 could challenge data center infrastructure capacity, potentially impacting adoption rates.
- Dependency on concentrated large-scale deployments by select partners (e.g., Microsoft, HUMAIN) introduces risk if these projects experience delays or policy changes.
- The markets competitive dynamics, including advancements by competitors such as AMD, could influence Nvidias market share in AI server hardware.
- Technological and logistical challenges in scaling liquid cooling solutions at hyperscale could affect the pace of AI data center expansion.