clm_7d3ccfbf0fa84d2a96039a069e676b55_r1

IX2-0169

① SA Source

• Source: 開啟完整 SA 文章
• Section: Nvidia versus AMD Disagg Prefill
• Line hint: 282

Context Before

Source: SemiAnalysis InferenceX ↗

Evidence

Once we bring Dynamo TRT-LLM into the equation, the B200’s performance is boosted even more to the point that the MI355X even with MTP can’t match the B200’s performance with Dynamo TRT-LLM and MTP

Context After

Source: SemiAnalysis InferenceX ↗

② Atomic Claim

一旦加入 Dynamo TRT-LLM，B200 效能進一步提高，使即使開啟 MTP 的 MI355X 也無法匹配搭配 Dynamo TRT-LLM 與 MTP 的 B200。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "UNSPECIFIED_ATTRIBUTE",
  "context_nodes": [
    {
      "id": "04_knowledge_base/NVIDIA B200",
      "label": "B200"
    },
    {
      "id": "04_knowledge_base/Multi-Token Prediction",
      "label": "MTP"
    },
    {
      "id": "04_knowledge_base/MI355X",
      "label": "MI355X"
    }
  ],
  "entity": {
    "id": "04_knowledge_base/Large language model",
    "label": "LLM"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "一旦加入 Dynamo TRT-LLM，B200 效能進一步提高，使即使開啟 MTP 的 MI355X 也無法匹配搭配 Dynamo TRT-LLM 與 MTP 的 B200。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	LLM	04_knowledge_base/Large language model
context_0	B200	04_knowledge_base/NVIDIA B200
context_1	MTP	04_knowledge_base/Multi-Token Prediction
context_2	MI355X	MI355X

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。