clm_ab2beabcd8494c7ba80600724c6266e7_r1

IX2-0219

① SA Source

• Source: 開啟完整 SA 文章
• Section: Nvidia Disagg Prefill and WideEP
• Line hint: 378

Context Before

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Evidence

For a model like DeepSeek R1, decode is memory-bandwidth-bound: the bottleneck is how fast GPUs can load weights from HBM

Context After

Source: SemiAnalysis InferenceX ↗

② Atomic Claim

對 DeepSeek R1 這類模型而言，decode 受到記憶體頻寬限制，瓶頸是 GPUs 從 HBM 載入 weights 的速度。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "BANDWIDTH",
  "context_nodes": [
    {
      "id": "04_knowledge_base/Decode",
      "label": "decode"
    },
    {
      "id": "04_knowledge_base/GPU",
      "label": "GPUs"
    },
    {
      "id": "04_knowledge_base/HBM",
      "label": "HBM"
    }
  ],
  "entity": {
    "id": "02_companies/DeepSeek",
    "label": "DeepSeek"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "對 DeepSeek R1 這類模型而言，decode 受到記憶體頻寬限制，瓶頸是 GPUs 從 HBM 載入 weights 的速度。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	DeepSeek	DeepSeek
context_0	decode	Decode
context_1	GPUs	GPU
context_2	HBM	HBM

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。