This calculator lets you explore what drives AI energy, carbon and water consumption, so you can see how different inputs affect impact.
It’s a simplified view of Greenpixie’s enterprise methodology – built for large-scale AI estates across AWS, Azure, and GCP.
Select the AI provider and model you are using. Different models have vastly different energy profiles — larger models like GPT-5.4 require significantly more compute than smaller alternatives.
Powered by Greenpixie's enterprise AI methodologyGreenpixie is an enterprise data company helping organisations understand and reduce the environmental impact of their cloud usage.
Today, we support customers across billions in cloud spend, providing FinOps-grade sustainability data to enable real business decisions, not just reporting. Our approach combines cost, carbon, energy and water into a single, consistent framework that can be used across teams, regions, and providers.
This means data that is:
AI is the next frontier of cloud usage. With this work, we are bringing AI into the same rigorous, enterprise-grade standard, with the same calculation considerations, enabling organisations to measure, compare, and optimise AI with the same level of confidence as the rest of their cloud estate.
Having been involved in the Sustainable AI Working Group and seen the methodology up close, it’s clear Greenpixie are setting the gold standard for how enterprises can measure the energy, carbon and water impact of AI workloads.
Mark Bradley
Flexera, Senior Manager, Product
I am enthusiastic and confident in the approach that Greenpixie has taken.
Wiebren van der Zee
Sustainable IT, Domain Expert (CIO office)
The methodology strikes the right balance between scientific rigour and enterprise usability. It gives organisations a credible way to understand and optimise the sustainability impact of AI, including consumption of water, kWh and carbon.
Andy Westbrook
NTT Data, Director, Cloud Transformation & FinOps
To build a reliable picture of AI energy usage, we carried out detailed benchmarking across a wide range of models and scenarios.
Frontier Model Benchmarking
Benchmarked 35 open-source frontier LLMs (FP8, INT8, NVFP4 quantised), ranging from 1 billion to 1 trillion parameters. This is one of the first studies to focus exclusively on quantised models at this scale, reflecting how models are increasingly deployed in modern hyperscaler environments.
GPU Test Infrastructure
Ran tests on H100 and B200 GPUs using specialised inference instances chosen for their performance, which are representative of cutting-edge infrastructure used in practice.
Real-World Task Simulation
Simulated a variety of real-world text-based tasks with different prompt and response lengths.
Batch Efficiency Analysis
Varied the number of parallel requests per batch and analysed how workload variables (including batching) affect energy efficiency.
Energy Measurement
Measured wall-clock time alongside GPU and CPU energy consumption per token, averaging results over many batches per data point for statistical robustness.
Inference Phase Profiling
Captured differences across inference phases (prefill, overlap, and decode) using timestamped measurements.
Emissions Data Generation
Generated thousands of kWh and amortised embodied emissions data points per input and output token.
Cache Impact Modelling
Estimated prefix caching hit rates in enterprise scenarios, and modelled the resulting energy scaling.
Parameter & VRAM Scaling
Measured how both energy and wall-clock time (used for amortisation) vary with active parameter count and VRAM requirements.
Proprietary Model Inference
Inferred proprietary model characteristics using third-party data and cross-validation against kWh per dollar metrics.
