clm_9e1a42d4e6aa446caf676d0550d7636a_r1

VR2-0755

① SA Source

• Source: 開啟完整 SA 文章
• Section: VR NVL72 TCO: BoM and Power Budget Analysis
• Line hint: 943

Context Before

Our AI Memory Model ↗ expects significant increases in LPDDR5 and DDR5 contract prices into 2Q26 and beyond and we expect to make further revisions higher in total server capex.

NVIDIA’s 2300W configuration represents the Max-P configuration, while the efficiency optimized Max-Q configuration runs at 1800W. Regardless of which configuration Nvidia claims both can hit the same peak clocks and therefore achieve marketed 50 PFLOPS FP4 performance. While the underlying hardware is the same, the TCO implications are due to operating costs from different levels of power consumption.

Evidence

Below we share detailed numbers on cost of servers, storage, networking, etc as well as what Nvidia plans to do with Groq

Context After

Source: SemiAnalysis AI TCO Model ↗

② Atomic Claim

文章後續提供 servers、storage、networking 等更詳細成本數字，也討論 Nvidia 計畫如何運用 Groq。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "additional_nodes": [],
  "frame_type": "RELATION",
  "object": {
    "id": "02_companies/Groq",
    "label": "Groq"
  },
  "predicate": "PROVIDES",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "subject": {
    "id": "02_companies/NVDA",
    "label": "Nvidia"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject	Nvidia	NVDA
object	Groq	Groq

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。