clm_a5a8abb11906448cac39dd4caf73b0ba_r1

IX2-0018

① SA Source

• Source: 開啟完整 SA 文章
• Section: Introduction
• Line hint: 29

Context Before

With today’s release, InferenceXv2 is now the first suite to benchmark the Blackwell Ultra GB300 NVL72 and B300 across the whole pareto frontier curve, and it is the first third party benchmark to test disagg+wideEP multi-node FP4 and FP8 MI355X performance. In future iterations of InferenceX, we will continue to focus heavily on disaggregated serving with wide expert parallelism as that is what is deployed in production at Frontier AI Labs like OpenAI, Anthropic, xAI, Google Deepmind, DeepSeek as well as advanced API providers like TogetherAI, Baseten, and Fireworks. In this article, we will also break down the system engineering principles and economics in play around the latest Claude Code Fast mode feature ↗.

Our benchmark is completely open-source under Apache 2.0 – this means that we are able to move at the same rapid speed at which the AI software ecosystem is advancing. If you like our work and would like to show us some support, please drop a star on our GitHub ↗! We also provide a free data visualizer at https://inferencex.com ↗ for everyone in the ML community to explore the complete dataset themselves.

Evidence

and Trainium3 to InferenceX later this year

Context After

Source: InferenceMAX GitHub ↗

② Atomic Claim

InferenceX 計畫在 2026 年稍晚加入 Trainium3。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "UNSPECIFIED_ATTRIBUTE",
  "context_nodes": [
    {
      "id": "04_knowledge_base/Trainium",
      "label": "Trainium3"
    }
  ],
  "entity": {
    "id": "04_knowledge_base/InferenceX",
    "label": "InferenceX"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "2026"
    ],
    "temporal_mentions": [
      "2026"
    ]
  },
  "value": {
    "numeric_mentions": [
      "2026"
    ],
    "value_text": "InferenceX 計畫在 2026 年稍晚加入 Trainium3。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	InferenceX	InferenceX
context_0	Trainium3	Trainium

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。