Epoch AI

Introducing the AI Chip Sales Data Explorer

2026-01-13 · 00:00 UTC ·The Epoch Ai Team ·1 min read

Brief

Epoch AI’s new explorer is a useful primary-source-style attempt to quantify the global installed base of AI accelerators, an increasingly important constraint for model training and deployment. By stitching together public disclosures and analyst evidence, it estimates both chip counts and compute capacity across major vendors, highlighting a rapid shift to Nvidia Blackwell parts and the infrastructure implications: multi–tens-of-billions in quarterly chip spend and power demand exceeding 10 GW just at the chip level.

Source evidence

title: Introducing the AI Chip Sales Data Explorer
author: The Epoch Ai Team
contenttype: article
publication: Epoch AI
published: 2026-01-13T00:00:00
sourceurl: https://epoch.ai/blog/introducing-the-ai-chip-sales-data-explorer

word_count: 327

Introduction

Discussions about AI progress increasingly hinge on computing capacity – aka compute – which is essential in order to develop, train, and deploy AI systems. But public data on the total capacity of AI computing hardware can be fragmented and incomplete.

To address this,
we are releasing a new AI Chip Sales data explorer, estimating and visualizing both the number and capacity of AI accelerators that have been sold or delivered in recent years. We leverage data and evidence from earnings reports, company disclosures, analyst coverage, and media reporting to produce estimates of AI chip counts across major vendors: Nvidia, Google, Amazon, AMD, and Huawei, broken down by AI chip model.

We believe this release provides the most complete publicly available picture to date on the global stock of AI compute.

Compute

We find that cumulative global AI compute capacity has reached the equivalent of more than 15 million Nvidia H100 GPUs, measured using each chip’s respective peak specifications in 8-bit operations per second.

Last year also saw major transitions due to new chip generations. Notably, Nvidia’s new Blackwell generation has largely displaced the H100 and H200, with the new B300 alone now accounting for the majority of AI compute capacity sold by Nvidia.

Costs and power

Acquiring and deploying these chips requires massive resources.

Overall, the cost to purchase these chips, even before auxiliary capital costs such as networking and data center construction, has rapidly escalated to tens of billions of dollars per quarter.

AI chips also demand a lot of electrical power. Even before accounting for the power overheads of servers and data centers, the total quantity of chips we track would draw over 10 GW of power. This is around twice the average power consumption of New York City.

We hope the AI Chip Sales data explorer serves as a useful tool to understand trends in global compute. To see our full datasets and interactive visualizations, visit the explorer here!