SA260324R-0223_r1

SA260324R-0223

① SA Source

• Source: 開啟完整 SA 文章
• Section: Oberon and Kyber Updates, Larger World Sizes Introduced, More Networking Updates
• Line hint: 274

Context Before

Nvidia provided a long-awaited update on its Kyber rack form factor, the latest addition to the lineup after Oberon having first been previewed as a prototype at GTC 2025. As a prototype, the rack architecture has continued to evolve, and we notice some changes. First, each compute blade has densified, with 4x Rubin Ultra GPU and 2x Vera each. There are a total of 2 canisters of 18 compute blades which amounts to 36 compute blades total for 144 GPUs in a rack.

Evidence

The initial Kyber design featured 2 GPUs and 2 Vera CPUs in one compute blade, with a total of 4 canisters of 18 compute blades each.

Context After

The details below are based on the Rubin Kyber prototypes, but Rubin Ultra will be redone.

② Atomic Claim

最初的 Kyber design，在每個 compute blade 中配置 2 顆 GPU 與 2 顆 Vera CPU，整櫃則有 4 個 canister、每個 canister 18 個 compute blade。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/NVIDIA Kyber Rack",
        "label": "Kyber"
      },
      "role": "rack"
    },
    {
      "node": {
        "id": "04_knowledge_base/GPU",
        "label": "GPU"
      },
      "role": "compute_device"
    },
    {
      "node": {
        "id": "04_knowledge_base/CPU",
        "label": "CPU"
      },
      "role": "host_processor"
    }
  ],
  "qualifiers": {
    "condition_text": "Vera、compute blade、canister 沒有 canonical mapping",
    "numeric_mentions": [
      "2",
      "2",
      "4",
      "18"
    ],
    "temporal_mentions": []
  },
  "relation_type": "INITIAL_KYBER_COMPUTE_BLADE_CONFIGURATION"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
rack	Kyber	04_knowledge_base/NVIDIA Kyber Rack
compute_device	GPU	GPU
host_processor	CPU	CPU

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。