SA260324R-0108
① SA Source
- Source: 開啟完整 SA 文章
- Section:
GPU and LPU Integration: Attention FFN Disaggregation (AFD) - Line hint:
101
Context Before
Source: MegaScale-Infer, SemiAnalysis
Evidence
As state-of-the-art mixture-of-expert (MoE) models grow increasingly sparse, tokens can choose experts from a larger expert pool. As a result, each expert receives fewer tokens, leading to lower utilization. This motivates attention and FFN disaggregation. If a GPU only performs attention operations, its HBM capacity can be fully allocated to KV cache, increasing the total number of tokens it can process, which then increases the tokens each expert processes on average.
Context After
Source: SemiAnalysis
② Atomic Claim
In the described sparse-MoE scaling scenario, an expanded expert pool gives each expert fewer tokens, reducing utilization.
- Epistemic Mode:
ASSERTED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"frame_type": "CLAIM_ONLY",
"qualifiers": {
"condition_text": "因 expert pool 擴大且模型更 sparse",
"numeric_mentions": [],
"temporal_mentions": []
},
"reason": "DEPENDENT_ON_PRIOR_MOE_CONTEXT_WITH_NO_CANONICAL_ENTITY_IN_FRAGMENT",
"statement": "In the described sparse-MoE scaling scenario, an expanded expert pool gives each expert fewer tokens, reducing utilization."
}④ Canonical Entity Mapping
- 此
CLAIM_ONLYrevision 沒有 canonical Entity mapping。
⑤ V2 Draft（需要修正時直接編輯）
Inline repair
只編輯以下三個 Draft 區塊。完成後將
review_action設為REQUEST_REPAIR，再手動執行 Repair Preview。
執行腳本前，Draft 不會寫入 Claim Store 或 Graph。
V2 Atomic Claim Draft
In the described sparse-MoE scaling scenario, an expanded expert pool gives each expert fewer tokens, reducing utilization.
V2 Semantic Frame Draft
{
"frame_type": "CLAIM_ONLY",
"qualifiers": {
"condition_text": "因 expert pool 擴大且模型更 sparse",
"numeric_mentions": [],
"temporal_mentions": []
},
"reason": "DEPENDENT_ON_PRIOR_MOE_CONTEXT_WITH_NO_CANONICAL_ENTITY_IN_FRAGMENT",
"statement": "In the described sparse-MoE scaling scenario, an expanded expert pool gives each expert fewer tokens, reducing utilization."
}V2 Revision Metadata Draft
{
"epistemic_mode": "ASSERTED",
"mapping_status": "PARTIAL"
}⑥ 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。