clm_928e3fcf01494af2a7006e4aa28476b4_r1

VR2R-0056::SPLIT01

回到這篇 SA Coverage

① SA Source

• Source: 開啟完整 SA 文章
• Section: Extreme Co-design: 6 Silicon Product Offerings under the Rubin Platform - Chip Floorplans & Specifications
• Line hint: 85

Context Before

Source: SemiAnalysis, Nvidia

Evidence

Rubin’s dense FP4 and FP8 FLOPs increase by roughly ~3.5× versus GB200, while FP16 FLOPs rise by a more modest ~1.6x, underscoring NVIDIA’s continued emphasis on FP4/FP8 as the primary scaling vector. On the memory side, HBM capacity remains flat from GB300, while HBM bandwidth scales more aggressively at ~2.8x. Overall, the architecture prioritizes bandwidth and low-precision compute.

Context After

Source: SemiAnalysis, Nvidia

② Atomic Claim

相較 GB200，Rubin 的 dense FP4 與 FP8 FLOPs 約提升 3.5 倍。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/Vera Rubin",
        "label": "Rubin"
      },
      "role": "subject_system"
    },
    {
      "node": {
        "id": "04_knowledge_base/GB200",
        "label": "GB200"
      },
      "role": "comparison_baseline"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP4",
        "label": "FP4"
      },
      "role": "metric"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP8",
        "label": "FP8"
      },
      "role": "metric"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "~3.5x"
    ],
    "temporal_mentions": []
  },
  "relation_type": "DENSE_LOW_PRECISION_FLOPS_SCALING"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject_system	Rubin	04_knowledge_base/Vera Rubin
comparison_baseline	GB200	GB200
metric	FP4	FP4
metric	FP8	FP8

⑤ V2 Draft（需要修正時直接編輯）

Inline repair

只編輯以下三個 Draft 區塊。完成後將 review_action 設為 REQUEST_REPAIR，再手動執行 Repair Preview。
執行腳本前，Draft 不會寫入 Claim Store 或 Graph。

V2 Atomic Claim Draft

相較 GB200，Rubin 的 dense FP4 與 FP8 FLOPs 約提升 3.5 倍。

V2 Semantic Frame Draft

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/Vera Rubin",
        "label": "Rubin"
      },
      "role": "subject_system"
    },
    {
      "node": {
        "id": "04_knowledge_base/GB200",
        "label": "GB200"
      },
      "role": "comparison_baseline"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP4",
        "label": "FP4"
      },
      "role": "metric"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP8",
        "label": "FP8"
      },
      "role": "metric"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "~3.5x"
    ],
    "temporal_mentions": []
  },
  "relation_type": "DENSE_LOW_PRECISION_FLOPS_SCALING"
}

V2 Revision Metadata Draft

{
  "epistemic_mode": "ASSERTED",
  "mapping_status": "COMPLETE"
}

⑥ 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。