{
  "title": "Compute in the 2018 report",
  "report_year": 2018,
  "period": "Historical snapshot: June 2018. Dates and populations are specified per row.",
  "methodology": "The speedup and cost examples concern specific training configurations. They do not establish a universal ranking of hardware or reflect present-day cloud prices.",
  "source_deck": "https://docs.google.com/presentation/d/e/2PACX-1vS_RSaVAQib2P8LtDxBJLCvdWlixK9WmxVhB9HUoNNsXdXR0Ts8Il1jB4967EiHGbxxLEYK1V540VRQ/pub",
  "web_edition_date": "2026-10-11",
  "rows": [
    {
      "measure": "Parallel-training hardware",
      "value": "32 GPUs",
      "definition": "Hardware count in the reported comparison with one GPU.",
      "printed_slide": null,
      "pdf_page": 11,
      "source_url": "https://www.stateof.ai/State-of-AI-Report-2018.pdf#page=11"
    },
    {
      "measure": "Reported training speedup",
      "value": "25x",
      "definition": "Speedup at the same accuracy in that comparison.",
      "printed_slide": null,
      "pdf_page": 11,
      "source_url": "https://www.stateof.ai/State-of-AI-Report-2018.pdf#page=11"
    },
    {
      "measure": "Cost-comparison target",
      "value": "75.7% top-1 accuracy",
      "definition": "ImageNet target used in the cloud-cost comparison.",
      "printed_slide": null,
      "pdf_page": 21,
      "source_url": "https://www.stateof.ai/State-of-AI-Report-2018.pdf#page=21"
    }
  ]
}