VR2R-0722_r1

VR2R-0722

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

The MI4XX currently has a performance per TCO advantage based on marketed dense FLOPS over VR. VR has a higher TCO per GPU compared to MI4XX, yet VR marketed PFLOPs are lower vs MI4XX resulting in a TCO per PFLOP disadvantage for the VR vs MI4XX.

Evidence

NVIDIA is offering sparsity for FP4, marketing 50 PFLOPS of FP4

Context After

, while AMD has opted to remove sparsity support since CDNA4 for inference dtypes. 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.

② Atomic Claim

NVIDIA 在 FP4 提供 sparsity，並行銷 50 PFLOPS FP4。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "02_companies/NVDA",
        "label": "NVIDIA"
      },
      "role": "subject"
    },
    {
      "node": {
        "id": "04_knowledge_base/FP4",
        "label": "FP4"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "04_knowledge_base/Sparsity",
        "label": "sparsity"
      },
      "role": "participant"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "50 PFLOPS"
    ],
    "temporal_mentions": []
  },
  "relation_type": "PROVIDES"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject	NVIDIA	NVDA
participant	FP4	FP4
participant	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。