VR2-0774
① SA Source
- Source: 開啟完整 SA 文章
- Section:
VR NVL72 TCO: BoM and Power Budget Analysis - Line hint:
981
Context Before
Source: SemiAnalysis AI TCO Model ↗
Evidence
implying a 10% theoretical compute disadvantage
Context After
Given that total cost of ownership is only 1.75x higher, this results in a superior performance-per-TCO profile for B300 despite the more modest marketed figures. Such a wildly different result despite very similar specs underscores that real world performance is not dictated by peak FLOPS or memory bandwidth alone. Software and network capabilities are also major factors that contribute to training and token throughput in real workloads.
Rubin and MI4XX will ship with new microarchitectures, real world performance is especially difficult to predict without and benchmarking like we do with InferenceX.
② Atomic Claim
- Epistemic Mode:
HYPOTHETICAL - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "COMPUTE_PERFORMANCE",
"context_nodes": [
{
"id": "04_knowledge_base/NVIDIA B300",
"label": "B300"
},
{
"id": "04_knowledge_base/MI355",
"label": "MI355"
}
],
"entity": {
"id": "04_knowledge_base/FP8",
"label": "FP8"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [
"10%"
],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [
"10%"
],
"value_text": "以 marketed dense FP8 compute 計，B300 理論上比 MI355 落後 10%。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | FP8 | FP8 |
| context_0 | B300 | 04_knowledge_base/NVIDIA B300 |
| context_1 | MI355 | MI355 |
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。