clm_344742e63cca411a86ba05e25d809255_r1

VR2-0109

① SA Source

• Source: 開啟完整 SA 文章
• Section: Rubin
• Line hint: 133

Context Before

With that said, many ML Systems engineer are still skeptical that this new form of sparsity will work well, and it is very possible that Nvidia’s 50 PFLOPS is purely marketing like prior generations

Rubin’s chip level TDP increases up to 2,300W vs 1000-1400W for Blackwell. Supply chain rumors have indicated that there are 2 different “SKUs” with different power and performance profiles: a Max-P variant at 2,300W and a Max-Q variant at 1,800W. However, these are not distinct hardware SKUs but the 2 default power profiles that Nvidia is offering users based on their workload needs. Max-Q is what Nvidia believes offers the best performance per Watt. Max-P offers the greatest absolute performance though this would come with an efficiency penalty. Running the Max-P setting results in a 20% increase in rack power draw but the performance gain fall well short of this 20% power consumption increase.

Evidence

Users can also choose whatever max power draw they prefer (as long as it is no more than 2,300W per GPU) and this has been the case for previous GPU generations as well

Context After

Source: Nvidia VR NVL72 Component BoM and Power Budget Model ↗

② Atomic Claim

使用者也可自行選擇最大 power draw，只要每顆 GPU 不超過 2,300W；過去 GPU 世代也具備類似彈性。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "POWER",
  "context_nodes": [],
  "entity": {
    "id": "04_knowledge_base/GPU",
    "label": "GPU"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "2,300W"
    ],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [
      "2,300W"
    ],
    "value_text": "使用者也可自行選擇最大 power draw，只要每顆 GPU 不超過 2,300W；過去 GPU 世代也具備類似彈性。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	GPU	GPU

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。