Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO2-eq Impact of AI Tokens for Open and Closed Weight Models
cs.CY, cs.LG
Submitted: 2026-09-27
Updated: 2026-09-29
Code: https://github.com/tatsu-lab/stanford_alpaca
Sources
- How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference
- From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs
- Power Hungry Processing: Watts Driving the Cost of AI Deployment?
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- Where Do the Joules Go? Diagnosing Inference Energy Consumption
- Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems
- Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms
- TokenPowerBench: Benchmarking the Power Consumption of LLM Inference
- Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference
- From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference
- Instruction-Following Evaluation for Large Language Models
- Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
- Training Verifiers to Solve Math Word Problems
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Humanity's Last Exam
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
- Evaluating Large Language Models Trained on Code
- Pointer Sentinel Mixture Models
- The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
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