clm_985363a8aad54ee59624c1eecf99c2da_r1

IX2-0476

① SA Source

• Source: 開啟完整 SA 文章
• Section: Optimizing Inference with Wide EP + Disaggregated Serving
• Line hint: 684

Context Before

Wide EP and disaggregated prefill are separate techniques that are often used together to achieve Pareto optimal performance. In this section, we walk through real results from InferenceX to build intuition for which combinations of parallelism strategy, wide EP, and disaggregated prefill are appropriate at different interactivity levels.

It helps to first understand what parallelism strategies fall on what parts of the Pareto frontier for single-node configurations. Take the example of DeepSeek R1 FP4 8k/1k on a single 8-GPU B200 node with TRT-LLM. The optimal strategy shifts as you move along the frontier, driven primarily by batch size and its effect on expert activation density.

Evidence

TP avoids this by sharding every expert across all GPUs, so all 8 GPUs participate equally in every expert computation regardless of which experts the router selects

Context After

Source: SemiAnalysis

② Atomic Claim

TP 可避免這個問題，因為每個 expert 都被分片到所有 GPUs，無論 router 選到哪些 experts，8 顆 GPUs 都會平均參與運算。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "COUNT",
  "context_nodes": [],
  "entity": {
    "id": "04_knowledge_base/GPU",
    "label": "GPUs"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "8"
    ],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [
      "8"
    ],
    "value_text": "TP 可避免這個問題，因為每個 expert 都被分片到所有 GPUs，無論 router 選到哪些 experts，8 顆 GPUs 都會平均參與運算。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	GPUs	GPU

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。