clm_343446cefae1483c9f100172fdd58500_r1

VR2-0753

① SA Source

• Source: 開啟完整 SA 文章
• Section: VR NVL72 TCO: BoM and Power Budget Analysis
• Line hint: 941

Context Before

Helios memory costs are more likely to be passed through or re-priced by assemblers and therefore exhibit greater hikes in a memory upcycle. Therefore, we model lower memory price hikes for VR and GB compared to MI4XX below. Our MI400 rack assumptions reflect $8.70/GB[[04_{k}nowledg{e}_{b}ase/LPDDR|LPDDR]]pricingfor[[02_{c}ompanies/AMD|AMD]]versus$6.77/GB for Nvidia, embedding volume discount structures vs the market contract price of $10.63/GB but reflecting the slack of volume economics vs NVIDIA.

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.

Evidence

Regardless of which configuration Nvidia claims both can hit the same peak clocks and therefore achieve marketed 50 PFLOPS FP4 performance

Context After

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

② Atomic Claim

無論採哪種 configuration，Nvidia 都宣稱兩者可達相同 peak clocks，因此都能達到行銷宣稱的 50 PFLOPS FP4 performance。

• Epistemic Mode: ATTRIBUTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "comparison_expression": "無論採哪種 configuration，Nvidia 都宣稱兩者可達相同 peak clocks，因此都能達到行銷宣稱的 50 PFLOPS FP4 performance。",
  "entities": [
    {
      "id": "02_companies/NVDA",
      "label": "Nvidia"
    },
    {
      "id": "04_knowledge_base/FP4",
      "label": "FP4"
    }
  ],
  "frame_type": "COMPARISON",
  "metric": "CLOCK_SPEED",
  "operator": "EQUAL_TO",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "50 PFLOPS"
    ],
    "temporal_mentions": []
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
comparison_entity_0	Nvidia	NVDA
comparison_entity_1	FP4	FP4

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。