clm_e89ced6baae444688dc970f83084873b_r1

IX2-0497

① SA Source

• Source: 開啟完整 SA 文章
• Section: Optimizing Inference with Wide EP + Disaggregated Serving
• Line hint: 698

Context Before

Source: SemiAnalysis InferenceX ↗

We observe the same general pattern when extending to wide EP with disaggregated prefill. Prefill and decode run with separate parallelism strategies and node counts, both tuned to the workload and target interactivity level. Take an 8k/1k workload (prefill heavy) at the high-throughput, low-interactivity end of the frontier. Prefill is the bottleneck as each request requires a forward pass of 8192 input tokens, which is computationally expensive. Recipes in this region allocate more prefill nodes than decode (4P1D, 7P2D, 4P3D) to sustain high prefill throughput. These prefill nodes run DEP configurations, replicating attention weights across independent data-parallel ranks so that multiple long-context prefills can be processed simultaneously. Decode nodes are fewer but run wide DEP with large batch sizes by the same principle as with single node.

Evidence

Recipes in this region shift to more decode nodes than prefill (1P3D, 1P4D)

Context After

② Atomic Claim

因此此區域 recipes 會配置比 prefill 更多的 decode nodes，例如 1P3D、1P4D。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "comparison_expression": "因此此區域 recipes 會配置比 prefill 更多的 decode nodes，例如 1P3D、1P4D。",
  "entities": [
    {
      "id": "04_knowledge_base/Prefill",
      "label": "prefill"
    },
    {
      "id": "04_knowledge_base/Decode",
      "label": "decode"
    }
  ],
  "frame_type": "COMPARISON",
  "metric": "COUNT",
  "operator": "UNSPECIFIED_COMPARISON",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "1"
    ],
    "temporal_mentions": []
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
comparison_entity_0	prefill	Prefill
comparison_entity_1	decode	Decode

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。