clm_1c4016a712da433485ad23f8f82d676f_r1

IX2-0128

① SA Source

• Source: 開啟完整 SA 文章
• Section: Disaggregated Inference Frameworks
• Line hint: 200

Context Before

Source: SemiAnalysis InferenceX ↗

Disaggregated Inference Frameworks

Evidence

It is inference-engine agnostic, allowing us to use SGLang and TRT LLM as backends in our benchmark

Context After

DeepSeek Disagg +WideEP Results Deep Dive

At almost all interactivity levels, disagg outperform aggregated inference (grey lines) in terms of total token throughput per GPU. Multi-node disaggregrated prefill framemogs single node aggregrated serving.

② Atomic Claim

Dynamo 與 inference engine 無關，因此 benchmark 可分別使用 SGLang 與 TRT LLM 作為 backend。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "additional_nodes": [],
  "frame_type": "RELATION",
  "object": {
    "id": "04_knowledge_base/Large language model",
    "label": "LLM"
  },
  "predicate": "USES",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "subject": {
    "id": "04_knowledge_base/SGLang",
    "label": "SGLang"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject	SGLang	SGLang
object	LLM	04_knowledge_base/Large language model

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。