A Silicon Data explanation of benchmark methodology

As GPUs become a foundational asset class in the AI economy, the need for transparent, high-fidelity benchmarks has never been more urgent. At Silicon Data,we have developed an indexing framework designed to standardize fragmented market data and produce more reliable pricing signals. This post highlights the core methodology and the sheer scale of data that powers the Silicon Data Index, offering a glimpse into how we transform millions of raw data points into actionable market intelligence.

Index at a glance

Currently, Silicon Data GPU indices track pricing for major AI GPU categories, including NVIDIA A100, H100, and B200 [BBG ticker: SDA100RT, SDH100RT, SDB200RT]. They are built on a massive, verified foundation to ensure it reflects the true state of global supply and demand. 

Comprehensive coverage of lease types 

What makes a benchmark credible 

GPU compute pricing is inherently multidimensional. Rates are dictated by a complex interplay of hardware configurations, provider, and geolocation. The economics of GPU rental are further influenced by the lease type.

When looking for a benchmark, there are approaches that you should avoid: 

• The simple average. The method glosses over the heterogeneity and hides underlying variability, making both lateral and longitudinal comparisons impossible. 

• Narrow specification. By focusing on too small a slice of the market, these indices miss broader dynamics, rendering them irrelevant for real-world needs and hard to attract liquidity.  

Instead, the Silicon Data indices are built on the following methodological principles. 

Built for the broad GPU market

Breadth matters because a relevant benchmark needs enough depth and diversity to capture how pricing actually forms across suppliers, regions, and commercial structures. Without this coverage, an index becomes too narrow to represent the market it claims to describe.

The Silicon Data GPU Index spans hundreds of thousands of verified pricing records across 50 countries and regions, with coverage from 50-100 platforms across hyperscalers, neoclouds, and marketplaces.

Precise characterization of pricing points 

The precision of each data point and the detailed specification of the underlying hardware unit is vital   because each parameter signals a different application, degree of flexibility, and risk dynamics

At Silicon Data, we capture and standardize:

• Pricing structures (lease type and durations)

• Providers and locations

• Granular hardware specification

While this level of data processing is massive and tedious, it is the only way to preserve the signal that makes an index truly informative.  

The critical step of normalization 

The challenge of building a benchmark is making heterogeneous market data comparable without stripping away its economic meaning. If records are blended without adjustment, the result no longer reflects true pricing dynamics. For instance, a short-duration Spot rental in one region is not economically equivalent to a multi-year Reserved contract in another. Blending them directly creates noise rather than insight.

Silicon Data uses a proprietary framework to normalize the prices of heterogeneous physical units to a standard configuration.  This ensures that only comparable pricing signals are aggregated, allowing for accurate analysis across different timeframes and locations.

A market-aligned benchmark

The transition of GPU compute from a simple utility to a pillar of the global AI economy requires robust benchmark indices to facilitate trading and risk management through increased transparency. By combining massive scale with rigorous normalization, the Silicon Data Index provides the clarity needed to navigate the complexity of the physical market.