{
  "docId": "019de50f-b45b-758c-ae28-b329ce7bd428",
  "docSlug": "8c3da2ef39d23af71506247c43b24336",
  "documentTitle": "NVIDIA | Investor Presentation Deck | 24 slides",
  "authorId": "nvidia",
  "authorName": "NVIDIA",
  "documentKindSlug": "conference-presentation",
  "documentKindLabel": "Conference presentation",
  "sourceTypeSlug": "investor_relations",
  "sourceTypeLabel": "Investor relations",
  "presentationDate": "2020-05-01 00:00:00",
  "orientation": "landscape",
  "aspectRatio": 2.4870467,
  "pageNumber": 2,
  "pageCount": 24,
  "prevPage": 1,
  "nextPage": 3,
  "slideType": "problem_statement",
  "function": "frame_problem",
  "density": "dense",
  "nDataPoints": 10,
  "notes": "The slide uses a split-screen approach to contrast training compute growth (left) with inference/interaction scale (right).",
  "elementsJson": null,
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019de50f-b45b-758c-ae28-b329ce7bd428/2",
  "deckHref": "/decks/019de50f-b45b-758c-ae28-b329ce7bd428",
  "deckJsonHref": "/decks/019de50f-b45b-758c-ae28-b329ce7bd428.json",
  "deckAnchorHref": "/decks/019de50f-b45b-758c-ae28-b329ce7bd428#slide-2",
  "components": [
    {
      "bbox": {
        "h": 0.55,
        "w": 0.45,
        "x": 0.05,
        "y": 0.28
      },
      "kind": "chart",
      "text": "Computing For Training AI",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "040e4809-898e-4ec0-889b-00328b316249",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.55,
        "w": 0.45,
        "x": 0.52,
        "y": 0.28
      },
      "kind": "image",
      "text": "AI Interactions Per Day",
      "attrs": null,
      "subkind": "icon-grid",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "97002be3-c46d-4b5d-bf93-62217fe36f5d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.38,
        "x": 0.08,
        "y": 0.84
      },
      "kind": "paragraph",
      "text": "3000X Higher Compute Required to Train Largest Models Since Volta",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "caddc4f4-beb6-4731-aed2-441a5a4f883c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.38,
        "x": 0.55,
        "y": 0.84
      },
      "kind": "paragraph",
      "text": "Every AI Powered Interaction Needs Varying Amount of Compute",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f9a06314-eef1-4087-b668-6a0b564f5e79",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.15,
        "x": 0.01,
        "y": 0.97
      },
      "kind": "source-note",
      "text": "Source: OpenAI and NVIDIA Analysis",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "53a82062-c92a-4b21-ba19-dc950e8d54db",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.5,
        "x": 0.25,
        "y": 0.12
      },
      "kind": "title",
      "text": "CHALLENGES: ACCELERATING BIG AND SMALL",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "42e71490-5d3d-46a8-a9b6-2c217b341ee6",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Problem Statement Canvas",
      "slug": "problem-statement-canvas",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "098c67fe-3f7c-4b6d-8434-d22cb4301106",
      "evidence": "paragraph/paragraph: 3000X Higher Compute Required to Train Largest Models Since Volta",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}