IX2-0002
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Introduction - Line hint:
19
Context Before
InferenceX v2: NVIDIA Blackwell Vs AMD vs Hopper - Formerly InferenceMAX
Introduction
Evidence
InferenceMAXv1 moved beyond static, point-in-time benchmarks by running continuous tests across hundreds of chips and popular open-source frameworks
Context After
Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer ↗ of compute from Google Cloud ↗ to Microsoft Azure ↗ to Oracle, OpenAI ↗, and many more.
InferenceXv2 builds on this foundation. It expands coverage to include large scale DeepSeek MoE disaggregated inference (disagg prefill, or simply “disagg”) with wide expert parallelism (wideEP) optimization to **all 6 NVIDIA western GPU SKUs from the past 4 years **as well as to every single AMD western GPU SKU released in the past 3 years – in total InferenceXv2 utilizes close to 1000 frontier GPUs for a full benchmark run across all SKUs.
② Atomic Claim
InferenceMAXv1 不再只做單一時間點的靜態 benchmark，而是持續測試數百款晶片與主流開源框架。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "LIFECYCLE_STATUS",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/InferenceX",
"label": "InferenceMAXv1"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "InferenceMAXv1 不再只做單一時間點的靜態 benchmark，而是持續測試數百款晶片與主流開源框架。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | InferenceMAXv1 | InferenceX |
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。