VR2-0766

① SA Source

Context Before

The MI4XX currently has a performance per TCO advantage based on marketed dense FLOPS over VR. VR has a higher TCO per GPU compared to MI4XX, yet VR marketed PFLOPs are lower vs MI4XX resulting in a TCO per PFLOP disadvantage for the VR vs MI4XX.

NVIDIA is offering sparsity for FP4, marketing 50 PFLOPS of FP4, while AMD has opsted to remove sparsity support since CDNA4 for inference dtypes. Harnessing the 50 PFLOPS sparse vs 35 PFLOPS dense drops the cost per perf in units of $/hr per Marketed PFLOP by 35% – a valid comparison if AI Labs can indeed successfully harness Sparse FP4 on the VR NVL72.

Evidence

Effective dense PFLOP (i.e. the real world chip throughput) can differ based on Model Flops Utilization % (MFU)

Context After

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

image

② Atomic Claim

Effective dense PFLOP,也就是真實世界 chip throughput,會隨 Model Flops Utilization(MFU)而不同。

  • Epistemic Mode: ASSERTED
  • Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "COMPUTE_PERFORMANCE",
  "context_nodes": [],
  "entity": {
    "id": "04_knowledge_base/Model FLOPS Utilization",
    "label": "Model Flops Utilization"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "Effective dense PFLOP,也就是真實世界 chip throughput,會隨 Model Flops Utilization(MFU)而不同。"
  }
}

④ Canonical Entity Mapping

RoleSurface LabelCanonical Target
entityModel Flops Utilization04_knowledge_base/Model FLOPS Utilization

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。