STATE OF AI REPORT COMPUTE INDEX.
🏠 Home 📧 Air Street PressThe State of AI Report Compute Index tracks the size of public, private and national HPC clusters, as well as the utilisation of various AI chips in AI research papers. As a key substrate upon which AI models are trained and run, the size of compute clusters and the popularity of specific chips helps us take a temperature check on the rate of progress of AI systems.
H100 GPU Count Last update: June 2025
Notes: Public Cloud = capacity rented from hyperscalers; Private Cloud = owned and run by the company; National HPC = government owned and run. Meta's H100 figure represents its H100 purchase as per company earnings call 1 Feb 2024. Excludes a further 250,000 H100 equivalents worth of GPU. Source document.
A100 GPU Count Last update: June 2025
Notes: Public Cloud = capacity rented from hyperscalers; Private Cloud = owned and run by the company; National HPC = government owned and run. Source document.
Cited chip usage in AI papers Last update: June 2025
Notes: This figure presents the number of open source AI papers that cite the use of specific AI chips according to analysis by Zeta Alpha. The "Big 6 Startups" category includes Habana, Graphcore, Cerebras, SambaNova Systemes, Cambricon and Groq. Figures for 2025 include counts as of 1 June 2025 and H2 predictions adjusted for volume.
Cited NVIDIA usage in AI papers Last update: June 2025
Notes: This figure presents the number of open source AI papers that cite the use of specific NVIDIA chips according to analysis by Zeta Alpha. Figures for 2025 include counts as of 1 June 2025 and H2 predictions adjusted for volume.
Cited AI chip startup usage in AI papers Last update: June 2025
Notes: This figure presents the number of open source AI papers that cite the use of specific AI chip startups according to analysis by Zeta Alpha. Figures for 2025 include counts as of 1 June 2025 and H2 predictions adjusted for volume.
Research topic signals by AI chip Last update: June 2025
Notes: The dataset covers 6,356 papers published between January 1 and June 1, 2025 and analysed using Zeta Alpha. Topic labels were assigned using GPT-4o-mini, and we filtered the results to include only chip-topic pairs with at least three papers. To focus the analysis on meaningful differentiation, we excluded topics whose percentage difference fell between -1 and +1 compared to the corpus-wide baseline. The results reveal clear skews between specific chips and research areas.
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