VR2R-0728

① SA Source

Context Before

As always, one caveat is that this comparison is done based on a marketed dense PFLOP basis. Effective dense PFLOP (i.e. the real world chip throughput) can differ based on Model Flops Utilization % (MFU), and in general we have seen NVIDIA chip operate at a higher MFU % vs AMD chips, suggesting that performance per TCO based on effective dense PFLOPs could be better for NVIDIA systems vs AMD—however, MFU is dependent on actual workloads with no one-size-fits-all MFU % that is consistently applicable to either systems.

Evidence

Indeed, real world use of FP4 Sparsity will probably not reach 50 PFLOPS

Context After

but it will probably deliver better effective FLOPs than FP4 Dense, but we have yet to evaluate what that real-world performance could be. Running VR NVL72 on 1800W would probably mean lower FP4 Sparse FLOPs than on 2300W.

② Atomic Claim

在真實世界使用情境下,FP4 Sparsity 的實際 performance 很可能無法達到 50 PFLOPS。

  • Epistemic Mode: ASSERTED
  • Mapping Status: COMPLETE

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/FP4",
        "label": "FP4"
      },
      "role": "datatype"
    },
    {
      "node": {
        "id": "04_knowledge_base/Sparsity",
        "label": "Sparsity"
      },
      "role": "feature"
    }
  ],
  "qualifiers": {
    "condition_text": "real-world usage expectation",
    "numeric_mentions": [
      "50 PFLOPS"
    ],
    "temporal_mentions": []
  },
  "relation_type": "REAL_WORLD_FP4_SPARSITY_BELOW_MARKETED_50_PFLOPS"
}

④ Canonical Entity Mapping

RoleSurface LabelCanonical Target
datatypeFP4FP4
featureSparsitySparsity

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。