IX2-0233
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Nvidia Disagg Prefill and WideEP - Line hint:
388
Context Before
Generally, TP is preferred at lower concurrencies due to load balancing. At small batch sizes, EP suffers from uneven token-to-expert routing, leaving some GPUs underutilized while others are overloaded. TP avoids this since each GPU holds a slice of every expert and always gets an equal share of work. At lower concurrency, the cost of this load imbalance outweighs TP’s additional communication overhead.
At higher concurrencies, this tradeoff changes. Expert activation becomes more evenly distributed across larger batch sizes, and EP’s communication and weight-loading advantages dominate over TP’s expensive per-layer all-reduce. In the middle of the curve, hybrid TP+EP configurations balance both concerns using small TP groups within each expert for load balancing while EP is used across the wider set of GPUs to amortize weights and reduce communication.
Evidence
For higher interactivity levels (low batch size), large scale-up world sizes tend not to deliver stronger performance
Context After
Prefill/decode disaggregation also plays a role. Prefill is compute-heavy and bursty; decode is memory-bandwidth-bound and steady-state. When they share the same GPUs, they interfere with each other, causing latency jitter and wasted capacity. Separating them onto dedicated GPU pools lets each run a workload matched to its characteristics, improving effective utilization. This is why disaggregated B200 configs outperform single-node B200 in the middle of the throughput-interactivity curve. PD separation combined with wider EP across more GPUs over IB amortizes weights more efficiently than cramming both phases onto a single 8-GPU node.
Side Note: the 10x inference engineers at TogetherAI noticed an pattern for multi-turn traffic where the requirements of first turn prefill is much different from the following turns prefill’s and disaggregrated it leading to better TTFT performance. ↗
② Atomic Claim
在較高 interactivity、也就是低 batch size 情況下，大型 scale-up world size 通常不會帶來更強效能。
- Epistemic Mode:
ASSERTED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"comparison_expression": "在較高 interactivity、也就是低 batch size 情況下，大型 scale-up world size 通常不會帶來更強效能。",
"entities": [
{
"id": "04_knowledge_base/Batch size",
"label": "batch size"
}
],
"frame_type": "COMPARISON",
"metric": "INTERACTIVITY",
"operator": "GREATER_THAN",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| comparison_entity_0 | batch size | 04_knowledge_base/Batch size |
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。