IX2-0129

① SA Source

Context Before

Source: SemiAnalysis InferenceX

Disaggregated Inference Frameworks

Evidence

For AMD, we use SGLang with two different KV cache transfer frameworks: MoRI and Mooncake

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

AMD,InferenceX 使用 SGLang 搭配兩種不同的 KV cache transfer frameworks:MoRIMooncake

  • Epistemic Mode: ASSERTED
  • Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "02_companies/AMD",
        "label": "AMD"
      },
      "role": "user_or_subject"
    },
    {
      "node": {
        "id": "04_knowledge_base/SGLang",
        "label": "SGLang"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/KV cache",
        "label": "KV cache"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/AMD MoRI backend",
        "label": "MoRI"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/Mooncake",
        "label": "Mooncake"
      },
      "role": "used_entity"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "relation_type": "USES"
}

④ Canonical Entity Mapping

RoleSurface LabelCanonical Target
user_or_subjectAMDAMD
used_entitySGLangSGLang
used_entityKV cache04_knowledge_base/KV cache
used_entityMoRI04_knowledge_base/AMD MoRI backend
used_entityMooncakeMooncake

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。