IX2-0444
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Disaggregated Prefill - Line hint:
662
Context Before
As always, the tradeoff is that of throughput versus latency. TP=8 within a group means those 8 GPUs now share a batch and must synchronize every decode step, reducing effective DP degree from 64 to 8. Per-GPU batching independence on the attention side is lost. But each DP group now processes attention 8x faster per step, since the matmul is split 8 ways across the TP group. Per-token latency drops while peak concurrency also drops, sliding the configuration along the latency-throughput Pareto frontier relative to pure DEP.
Disaggregated Prefill
Evidence
This is a compute-intensive operation as all tokens feed through the forward pass in parallel
Context After
In traditional single-node inference, engines interleave prefill and decode on the same GPUs. Incoming prefill requests stall in-flight decode batches, increasing both time-to-first-token and inter-token latency. Chunked prefill mitigates this by breaking long prefills into smaller pieces, but the fundamental resource contention remains. Disaggregated prefill eliminates this entirely!
② Atomic Claim
關於 disaggregated prefill：因為所有 tokens 都同時通過 forward pass，因此這是 compute-intensive operation。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "COMPUTE_PERFORMANCE",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/Disaggregated prefill",
"label": "disaggregated prefill"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "關於 disaggregated prefill：因為所有 tokens 都同時通過 forward pass，因此這是 compute-intensive operation。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | disaggregated prefill | 04_knowledge_base/Disaggregated prefill |
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。