VR2R-0724_r1

VR2R-0724

① SA Source

• Source: 開啟完整 SA 文章
• Section: VR NVL72 TCO: BoM and Power Budget Analysis
• Line hint: 633

Context Before

NVIDIA is offering sparsity for FP4, marketing 50 PFLOPS of FP4, while AMD has opted to remove sparsity support since CDNA4 for inference dtypes.

Evidence

Harnessing the 50 PFLOPS sparse vs 35 PFLOPS dense drops the cost per perf in units of $/hr per Marketed PFLOP by 35%—a valid comparison if AI Labs can indeed successfully harness Sparse FP4 on the VR NVL72.

Context After

As always, one caveat is that this comparison is done based on a marketed dense PFLOP basis. Effective dense PFLOP (i.e. the real world chip throughput) can differ based on Model Flops Utilization % (MFU), and in general we have seen NVIDIA chip operate at a higher MFU % vs AMD chips, suggesting that performance per TCO based on effective dense PFLOPs could be better for NVIDIA systems vs AMD—however, MFU is dependent on actual workloads with no one-size-fits-all MFU % that is consistently applicable to either systems.

② Atomic Claim

若 AI Labs 能在 VR NVL72 上有效利用 Sparse FP4，從 35 PFLOPS dense 提升到 50 PFLOPS sparse，可讓以每小時每 marketed PFLOP 計算的 cost/performance 降低約 35%。

• Epistemic Mode: HYPOTHETICAL
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/VR NVL72",
        "label": "VR NVL72"
      },
      "role": "platform"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP4",
        "label": "FP4"
      },
      "role": "datatype"
    },
    {
      "node": {
        "id": "04_knowledge_base/Sparsity",
        "label": "sparsity"
      },
      "role": "feature"
    }
  ],
  "qualifiers": {
    "condition_text": "valid only if AI Labs successfully harness Sparse FP4 on VR NVL72",
    "numeric_mentions": [
      "50 PFLOPS",
      "35 PFLOPS",
      "35%"
    ],
    "temporal_mentions": []
  },
  "relation_type": "SPARSE_FP4_POTENTIAL_COST_PER_PERFORMANCE_IMPROVEMENT"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
platform	VR NVL72	04_knowledge_base/VR NVL72
datatype	FP4	FP4
feature	sparsity	Sparsity

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。