clm_264934fec27c4bccadd2fc6a7e9a39a0_r1

VR2-0750

① SA Source

• Source: 開啟完整 SA 文章
• Section: VR NVL72 TCO: BoM and Power Budget Analysis
• Line hint: 939

Context Before

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.

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.

Evidence

we expect to make further revisions higher in total server capex

Context After

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.

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

關於 LPDDR5：SemiAnalysis 預期之後還會進一步上修 total server capex estimates。

• Epistemic Mode: EXPECTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "COST",
  "context_nodes": [],
  "entity": {
    "id": "04_knowledge_base/LPDDR5",
    "label": "LPDDR5"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "關於 LPDDR5：SemiAnalysis 預期之後還會進一步上修 total server capex estimates。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	LPDDR5	LPDDR5

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。