clm_872201061dc54b14899619af5fdb1f2e_r2

VR2R-0711

回到這篇 SA Coverage

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

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.

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

NVIDIA 的 2300W configuration 是 Max-P，而以效率最佳化的 Max-Q configuration 則運作在 1800W。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "MAX_P_MAX_Q_POWER_CONFIGURATION",
  "context_nodes": [],
  "entity": {
    "id": "02_companies/NVDA",
    "label": "NVIDIA"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": "Max-P and Max-Q are plain text configuration labels",
    "numeric_mentions": [
      "2300W",
      "1800W"
    ],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [
      "2300W",
      "1800W"
    ],
    "value_text": "Max-P is 2300W while efficiency-optimized Max-Q is 1800W."
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	NVIDIA	NVDA

⑤ V2 Draft（需要修正時直接編輯）

Inline repair

只編輯以下三個 Draft 區塊。完成後將 review_action 設為 REQUEST_REPAIR，再手動執行 Repair Preview。
執行腳本前，Draft 不會寫入 Claim Store 或 Graph。

V2 Atomic Claim Draft

NVIDIA 的 2300W configuration 是 Max-P，而以效率最佳化的 Max-Q configuration 則運作在 1800W。

V2 Semantic Frame Draft

{
  "attribute": "MAX_P_MAX_Q_POWER_CONFIGURATION",
  "context_nodes": [],
  "entity": {
    "id": "02_companies/NVDA",
    "label": "NVIDIA"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": "Max-P and Max-Q are plain text configuration labels",
    "numeric_mentions": [
      "2300W",
      "1800W"
    ],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [
      "2300W",
      "1800W"
    ],
    "value_text": "Max-P is 2300W while efficiency-optimized Max-Q is 1800W."
  }
}

V2 Revision Metadata Draft

{
  "epistemic_mode": "ASSERTED",
  "mapping_status": "PARTIAL"
}

⑥ 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。