clm_099703f0fcfb4a71ba381cd87ad95251_r1

VR2-0605

① SA Source

• Source: 開啟完整 SA 文章
• Section: GB200 Hardware Architecture - Component Supply Chain & BOM
• Line hint: 731

Context Before

Vera Rubin NVL72 Scale-up Topology. Source: SemiAnalysis AI Networking Model ↗

Evidence

The second meaningful difference between the NVL72 and HGX deployments is that the former has a scale-out bandwidth of 1.6T per GPU while the latter only has a scale-out bandwidth of 800G per GPU

Context After

The HGX Rubin NVL8 server consists of eight 800G CX-9 NIC packages – one NIC per GPU – which means that the scale-out bandwidth does not increase from its predecessor, the HGX B300 server. The Vera Rubin NVL72 deployment on the other hand doubles the per GPU scale-out bandwidth to 1.6T, but not by doubling the bandwidth per NIC. Rather, the “1.6T NIC” attached to each Rubin chip is comprised of two 800G CX-9 packages that is connected to the Vera CPU by PCIe Gen 6.0 lanes.

Each compute tray on the VR NVL72 has eight 800G CX-9 NICs, but there are two possibilities for the number of OSFP cages - either one 1.6T OSFP cage per GPU for a total of 4 per compute tray, or two 800G OSFP cages per GPU for a total of 8 cages per compute tray. We think that the latter would be the more popular deployment assumption, and will be the base case for our discussion of scale-out networking architectures in later sections of the article.

② Atomic Claim

NVL72 與 HGX 部署的第二項重要差異，是前者每顆 GPU scale-out bandwidth 為 1.6T，後者每顆 GPU 僅為 800G。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/HGX",
        "label": "HGX"
      },
      "role": "subject"
    },
    {
      "node": {
        "id": "04_knowledge_base/GPU",
        "label": "GPU"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "04_knowledge_base/1.6T",
        "label": "1.6T"
      },
      "role": "participant"
    },
    {
      "node": {
        "id": "04_knowledge_base/800G",
        "label": "800G"
      },
      "role": "participant"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "1.6",
      "800"
    ],
    "temporal_mentions": []
  },
  "relation_type": "DEPLOYS"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
subject	HGX	HGX
participant	GPU	GPU
participant	1.6T	1.6T
participant	800G	800G

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。