clm_817d458f05d8432f897cbedee6503431_r1

VR2-0730

① SA Source

• Source: 開啟完整 SA 文章
• Section: VR NVL72 TCO: BoM and Power Budget Analysis
• Line hint: 931

Context Before

Source: VR NVL72 Component BoM and Power Budget Model ↗

Evidence

For example VR NVL 72 Hyperscaler Arista has a capital cost of $3.28to[[04_{k}nowledg{e}_{b}ase/MI400|MI4XX]][[04_{k}nowledg{e}_{b}ase/Hyperscaler|Hyperscaler]]of$2.86 per hour per GPU over a 4 year useful life

Context After

However, one advantage for Nvidia’s VR SOCAMM option is that NVIDIA directly procures memory, allowing them to negotiate long-term agreements, volume-preferential terms with memory suppliers and most importantly, VVIP pricing. We think this will shield end customers from spikes in memory costs as we outline in our AI server apocalypse note ↗, and is another example of how, as the Central Bank of AI ↗, Nvidia is effectively hedging DRAM prices for all of its customers.

By contrast, AMD is much more exposed to DRAM price increases as it has about double the amount of DRAM, with about 55 TB per rack of LPDDR5 and 55 TB per rack of DDR5. For the AMD’s Helios rack scale system, AMD sells the GPU/board and does procure the LPDDR5 memory, but it does not procure DDR5 DRAM for rack compute trays; rack assemblers/ODMs source and integrate DDR5 memory. This leaves buyers of AMD’s racks more exposed because AMD is only able to potentially “hedge” the LPDDR5 portion via long-term contracts leaving the DDR5 portion completely exposed. Having double the DRAM content also nearly doubles the overall exposure.

② Atomic Claim

例如，VR NVL72 部署於 Hyperscaler、使用 Arista networking 時，在 4 年 useful life 下，每顆 GPU 每小時 capital cost 為 3.28 美元；MI4XX Hyperscaler 則為 2.86 美元。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/Hyperscaler",
        "label": "Hyperscaler"
      },
      "role": "user_or_subject"
    },
    {
      "node": {
        "id": "02_companies/Arista",
        "label": "Arista"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/GPU",
        "label": "GPU"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/MI400",
        "label": "MI4XX"
      },
      "role": "used_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/VR NVL72",
        "label": "VR NVL72"
      },
      "role": "context_entity"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "4",
      "3.28",
      "2.86"
    ],
    "temporal_mentions": []
  },
  "relation_type": "USES"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
user_or_subject	Hyperscaler	Hyperscaler
used_entity	Arista	Arista
used_entity	GPU	GPU
used_entity	MI4XX	MI400
context_entity	VR NVL72	04_knowledge_base/VR NVL72

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。