clm_8f72be88ef6e4711af7aa8b6634e5482_r1

IX2-0569

① SA Source

• Source: 開啟完整 SA 文章
• Section: Future of InferenceX
• Line hint: 800

Context Before

All in all, GitHub Actions is just alright. It provides a painfully average experience for developers. It is certainly not meant for launching thousands of jobs across a fleet of hundreds of GPUs. Nevertheless, we have worked closely with some GitHub Actions engineers since our launch to better meet the needs of InferenceX, and we can confidently say they have been a pleasure to work with. Moreover, one of our direct asks was to implement lazy loading for jobs when clicking on a workflow run and, while it did take them a while, they eventually implemented the feature. ↗

Future of InferenceX

Evidence

These changes enabled us to seamlessly integrate PD-disagg benchmarks for H100, H200, B200, B300, GB200, GB300, and MI355X

Context After

Although we have made many improvements since our release, there is still much work to be done to achieve the north star goal of providing the most real-world inference benchmarks possible. To achieve this goal, we plan to benchmark on real datasets, add an agentic coding performance benchmark, include more SOTA inference optimizations, benchmark more models, and so much more.

Migration to Multi Turn Real Multi-Turn Chat and Agentic Coding Datasets

② Atomic Claim

這些改動讓團隊能順利整合 H100、H200、B200、B300、GB200、GB300 與 MI355X 的 PD-disagg benchmarks。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "frame_type": "NARY_RELATION",
  "participants": [
    {
      "node": {
        "id": "04_knowledge_base/H100",
        "label": "H100"
      },
      "role": "system_or_subject"
    },
    {
      "node": {
        "id": "04_knowledge_base/H200",
        "label": "H200"
      },
      "role": "integrated_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/NVIDIA B200",
        "label": "B200"
      },
      "role": "integrated_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/NVIDIA B300",
        "label": "B300"
      },
      "role": "integrated_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/GB200",
        "label": "GB200"
      },
      "role": "integrated_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/GB300",
        "label": "GB300"
      },
      "role": "integrated_entity"
    },
    {
      "node": {
        "id": "04_knowledge_base/MI355X",
        "label": "MI355X"
      },
      "role": "integrated_entity"
    }
  ],
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "relation_type": "INTEGRATES"
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
system_or_subject	H100	H100
integrated_entity	H200	H200
integrated_entity	B200	04_knowledge_base/NVIDIA B200
integrated_entity	B300	04_knowledge_base/NVIDIA B300
integrated_entity	GB200	GB200
integrated_entity	GB300	GB300
integrated_entity	MI355X	MI355X

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。