IX2-0129
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
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
對 AMD,InferenceX 使用 SGLang 搭配兩種不同的 KV cache transfer frameworks:MoRI 與 Mooncake。
- 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
| Role | Surface Label | Canonical Target |
|---|---|---|
| user_or_subject | AMD | AMD |
| used_entity | SGLang | SGLang |
| used_entity | KV cache | 04_knowledge_base/KV cache |
| used_entity | MoRI | 04_knowledge_base/AMD MoRI backend |
| used_entity | Mooncake | Mooncake |
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。