# Trends in AI Supercomputers

arXiv: https://arxiv.org/abs/2504.16026 · HTML: https://mlsanity.com/paper/2504.16026/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 109 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [The rising costs of training frontier AI models](https://mlsanity.com/paper/2405.21015/) (2024)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models](https://mlsanity.com/paper/2310.09259/) (2023)
- [The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?](https://mlsanity.com/paper/2401.02452/) (2024)
- [Algorithmic progress in computer vision](https://mlsanity.com/paper/2212.05153/) (2022)
- [Nuclear Arms Control Verification and Lessons for AI Treaties](https://mlsanity.com/paper/2304.04123/) (2023)
- [A Brief History of Recommender Systems](https://mlsanity.com/paper/2209.01860/) (2022)
- [Compute at Scale: A Broad Investigation into the Data Center Industry](https://mlsanity.com/paper/2311.02651/) (2023)
- [Green HPC: An analysis of the domain based on Top500](https://mlsanity.com/paper/2403.17466/) (2024)

## Built on by (most cited first, 4 of 4)

- [Technical Requirements for Halting Dangerous AI Activities](https://mlsanity.com/paper/2507.09801/) (2025)
- [Turing Test 2.0: The General Intelligence Threshold](https://mlsanity.com/paper/2505.19550/) (2025)
- [LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites](https://mlsanity.com/paper/2605.05615/) (2026)
- [The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI](https://mlsanity.com/paper/2608.05173/) (2026)

## Abstract (arXiv)

Frontier AI development relies on powerful AI supercomputers, yet analysis of these systems is limited. We create a dataset of 500 AI supercomputers from 2019 to 2025 and analyze key trends in performance, power needs, hardware cost, ownership, and global distribution. We find that the computational performance of AI supercomputers has doubled every nine months, while hardware acquisition cost and power needs both doubled every year. The leading system in March 2025, xAI's Colossus, used 200,000 AI chips, had a hardware cost of 7B, and required 300 MW of power, as much as 250,000 households. As AI supercomputers evolved from tools for science to industrial machines, companies rapidly expanded their share of total AI supercomputer performance, while the share of governments and academia diminished. Globally, the United States accounts for about 75% of total performance in our dataset, with China in second place at 15%. If the observed trends continue, the leading AI supercomputer in 2030 will achieve 2×1022 16-bit FLOP/s, use two million AI chips, have a hardware cost of 200 billion, and require 9 GW of power. Our analysis provides visibility into the AI supercomputer landscape, allowing policymakers to assess key AI trends like resource needs, ownership, and national competitiveness.