VR2R-0091
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Rubin - Line hint:
79
Context Before
While models that utilize Post Training Quantization or Quantization Aware Training will be tuned to maximize adaptive compression speedups, they are not strictly needed to take advantage of dynamic compression.
Evidence
This means the sparser the workload, the closer the performance will be to the 50 PFLOPS marketed peak performance.
Context After
NVIDIA thus brands the 50 PFLOPS figure as FP4 Inference while the 35 PFLOPS FP4 Training number is for dense workloads. As accuracy is preserved, this allows the marketing team to claim 5x FLOPs for Rubin over GB200, comparing 50 PFLOPS dynamically compressed FP4 to 10 PFLOPS dense FP4. Whether actual GEMM performance reaches 50 PFLOPS depends on how many zeros are in the tensor.
② Atomic Claim
因此 workload 越 sparse，實際 performance 就越接近宣傳的 50 PFLOPS peak performance。
- Epistemic Mode:
INFERRED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"frame_type": "CLAIM_ONLY",
"qualifiers": {
"condition_text": "承接 adaptive compression discussion",
"numeric_mentions": [
"50 PFLOPS"
],
"temporal_mentions": []
},
"reason": "WORKLOAD_AND_PEAK_PERFORMANCE_SUBJECTS_NOT_CANONICALIZED",
"statement": "workload 越 sparse，performance 越接近 50 PFLOPS marketed peak。"
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| — | — | — |
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。