This dataset captures 482 AI supercomputer installations in a single, self-contained table with no relational dependencies—a complete census of high-performance computing assets that achieves 86% data completeness and perfect referential integrity. The architecture supports immediate analysis of hardware specifications, vendor ecosystems, and deployment patterns without the complexity of cross-table joins. However, the **Noteworthy** field is 98% null (473 of 482 records empty), effectively eliminating qualitative context that might explain outlier configurations, strategic partnerships, or deployment rationale. This gap constrains root-cause analysis when performance or cost anomalies surface. The dataset excels at quantitative benchmarking—comparing compute capacity, cost structures, and vendor market share across the global AI infrastructure landscape—and can reliably answer "what" and "how many" questions about hardware deployments. It cannot answer "why" certain systems were commissioned, which business outcomes they target, or how utilization tracks against capacity, because operational telemetry and strategic annotations are absent. Decision-makers can use this intelligence to validate vendor negotiations, identify capability gaps in their own infrastructure, and spot emerging hardware trends, but should not expect insight into ROI, workload efficiency, or the business cases that justified these capital investments.