# Inputs and Evidence Sources | AI methodology
Source: https://greenpixie.com/docs/methodology/ai/inputs-and-evidence-sources

The data categories and evidence sources the methodology draws on.

The methodology draws on the following categories of inputs.

## 5.1 Hardware Benchmarking Data [#51-hardware-benchmarking-data]

Energy measurements collected from physical AI inference workloads running on real GPU hardware. GPU-board power is captured via the NVIDIA Management Library (NVML), sampled at an interval of 10 milliseconds, with non-GPU server energy estimated from the inference-phase wall time and the power specification of the host server.

## 5.2 Server Specification Data [#52-server-specification-data]

Published hyperscaler AI instance specifications, used as the reference configuration for estimating the non-GPU server contribution, covering processor generation, core count, memory capacity, and the resulting power draw at a given accelerator count. The processor operating point follows the default applied in the core Greenpixie compute methodology.

## 5.3 Model and Architecture Data [#53-model-and-architecture-data]

Publicly available and vendor-published information about AI model characteristics, including parameter counts, active parameter counts for mixture-of-experts (MoE) architectures, quantisation levels, and VRAM requirements. For open-weights models, this information is typically available directly from model documentation and hosting platforms.

For proprietary models where architecture details are not publicly disclosed, the methodology establishes a plausible range for each characteristic that cannot be observed. These ranges draw on provider tier naming conventions, the published distribution of frontier open-weights releases, independent datasets of notable model parameter counts, published throughput measurements, and the architectural characteristics of comparable open-weights models. The banding and sampling process is described in Sections 6.4 and 7.6.

## 5.4 Cloud Provider Billing and Pricing Data [#54-cloud-provider-billing-and-pricing-data]

Publicly available pricing structures and billing configurations from AWS, Azure, and GCP, used to identify token-type distinctions, processing tiers, and caching discounts, and used in the price-ratio validation described in Section 9.5.

## 5.5 Embodied Emissions Data [#55-embodied-emissions-data]

Hardware manufacturing and lifecycle emissions data adapted from the Boavizta methodology and associated formulae for server and GPU cradle-to-grave embodied emissions calculations.

## 5.6 Carbon Intensity Data [#56-carbon-intensity-data]

Regional and hourly electricity grid carbon intensity factors, as described in the core [*Greenpixie Methodology for Cloud Emission Measurement*](/methodology/cloud).

## 5.7 Water Consumption Data [#57-water-consumption-data]

Cloud provider Water Usage Effectiveness values and regional water intensity factors, as set out in the [*Greenpixie Methodology for Cloud Emission Measurement*](/methodology/cloud).

## 5.8 Published Research and Industry Sources [#58-published-research-and-industry-sources]

Publicly available research from institutions and organisations including the International Energy Agency, Google, Mistral AI, Epoch AI, Artificial Analysis, MIT Technology Review, and academic inference-energy benchmarking literature, used to inform contextual understanding, methodological design, and the external comparisons described in Section 9.4. A full bibliography is provided in Section 12.