VR2R-0067_r1

VR2R-0067

① SA Source

• Source: 開啟完整 SA 文章
• Section: Rubin
• Line hint: 61

Context Before

Additionally, Nvidia claims up to an effective 50 PFLOPS of FP4 performance can be achieved with an updated 3rd generation Transformer Engine that replaces 2:4 structured sparsity from prior generations. We will detail this architectural feature for Rubin below.

Evidence

Notably, the Tensor core width doubling only applies to FP4 and FP8, with BF16 and TF32 remaining the same as Blackwell, resulting in performance scaling only 1.6x of Blackwell.

Context After

This architectural decision reflects NVIDIA’s belief that most training and inference workloads will move away from TF32 and BF16 and onto FP8 and FP4.

② Atomic Claim

Tensor Core width 加倍只適用於 FP4 與 FP8；BF16 與 TF32 則維持與 Blackwell 相同，因此效能僅約為 Blackwell 的 1.6 倍。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "comparison_expression": "Tensor Core width doubling applies only to FP4/FP8; BF16/TF32 remain at Blackwell levels and performance scales about 1.6x Blackwell.",
  "entities": [
    {
      "id": "04_knowledge_base/FP4",
      "label": "FP4"
    },
    {
      "id": "04_knowledge_base/FP8",
      "label": "FP8"
    },
    {
      "id": "04_knowledge_base/BF16",
      "label": "BF16"
    },
    {
      "id": "04_knowledge_base/TF32",
      "label": "TF32"
    },
    {
      "id": "04_knowledge_base/Blackwell",
      "label": "Blackwell"
    }
  ],
  "frame_type": "COMPARISON",
  "metric": "TENSOR_CORE_WIDTH_AND_PERFORMANCE_SCALING",
  "operator": "MIXED_SCALING",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "2x width",
      "1.6x"
    ],
    "temporal_mentions": []
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	FP4	FP4
entity	FP8	FP8
entity	BF16	BF16
entity	TF32	TF32
entity	Blackwell	Blackwell

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。