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# Training Grok 3 drew an estimated 110 MW, the most of any model with published data
- URL: https://aiadoption.org/data/power-draw-to-train-frontier-models/
- Published: 2026-10-19T14:40:46.000Z
- Updated: 2026-10-21T06:01:31.000Z
- Description: Training xAI's Grok 3 drew an estimated 110 MW of power, about 450,000 times the 246 W of a 2011 model in the data.
- Author: Manu
- Tags: Data, AI Infrastructure, #chart, #ai-index-2026, #hub-infrastructure

110 MW

estimated power draw while training Grok 3 (Feb 2025); Llama 4 Behemoth needed about 44 MW

Largest training power draw to date (log scale)

20122025

## What the chart shows

The power needed to run a frontier training run has grown by several orders of magnitude. Grok 3 (February 2025) drew an estimated 110 megawatts during training, the most of any model in the data. Llama 4 Behemoth needed about 44 MW and Llama 3.1-405B about 23 MW.

The earliest model in the series, a 2011 deep autoencoder, drew about 246 watts. Power draw is not available for many of the newest models, because developers disclose too little.

## Key numbers

| Measure                     | Value  |
| --------------------------- | ------ |
| Grok 3 (Feb 2025)           | 110 MW |
| Llama 4 Behemoth (Apr 2025) | 44 MW  |
| Llama 3.1-405B (Jul 2024)   | 23 MW  |
| GPT-4 (Mar 2023)            | 20 MW  |
| GPT-3 175B (2020)           | 5.1 MW |

## How to read it

- Power draw is the estimated electrical load of the training hardware cluster, not total energy used.
- Only 34 models have enough public information for an estimate. Hover over a dot to see the model.
- The vertical axis is logarithmic.

**Source:** [Epoch AI](https://aiadoption.org/sources/#epoch-ai), 2026, via the AI Index Report 2026 (Stanford HAI), figure 1.4.2: *Total power draw required to train frontier models, 2011–25*. Chart and text by AI Adoption.  
Numbers checked against the published data on 19 October 2026.  
[Download the data (CSV)](https://charts.aiadoption.org/ai-index/csv/power-draw-to-train-frontier-models.csv?ref=aiadoption.org) · [Cite this chart](#source)