clm_060aafd30af04915af587d089968ae22_r1

IX2-0264

① SA Source

• Source: 開啟完整 SA 文章
• Section: Nvidia Blackwell Perf TCO Analysis - B100 vs B200 vs GB200NVL72
• Line hint: 440

Context Before

Source: SemiAnalysis InferenceX ↗

Evidence

Google TPU, AWS Trainium and Nvidia are the only AI chips to have rack scale system designs deployed today

Context After

Source: SemiAnalysis InferenceX ↗

② Atomic Claim

目前已實際部署 rack-scale system designs 的 AI chips 只有 Google TPU、AWS Trainium 與 Nvidia。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "02_companies/GOOG",
        "label": "Google"
      },
      "role": "subject"
    },
    {
      "node": {
        "id": "04_knowledge_base/TPU",
        "label": "TPU"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "04_knowledge_base/AWS",
        "label": "AWS"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "04_knowledge_base/Trainium",
        "label": "Trainium"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "02_companies/NVDA",
        "label": "Nvidia"
      },
      "role": "participant"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "relation_type": "DEPLOYS"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject	Google	GOOG
participant	TPU	TPU
participant	AWS	AWS
participant	Trainium	Trainium
participant	Nvidia	NVDA

⑤ Human Review

請在 Properties 逐項確認：

• 原文 → Atomic Claim 是否忠實
• Atomic Claim → Semantic Frame 是否忠實
• Canonical Entity mapping 是否正確
• Epistemic mode 是否保留原文語氣
• 最後選擇 review_action

Review state

Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。