clm_747b65e9f5f1403eaeb2f105c885b832_r1

IX2-0290

① SA Source

• Source: 開啟完整 SA 文章
• Section: AMD Composability Issue on FP4, Distributed Inferencing and Wide Expert Parallelism
• Line hint: 486

Context Before

Subscribed

AMD software is still not meeting the mark, and the theoretical speed of light modelling at SemiAnalysis and at AMD show that for FP4, disaggregated inferencing with wide expert parallelism should perform better than inference on a single node of MI355X. Unfortunately, Software continues to be a massive bottleneck for AMD GPUs. AMD management needs to continue to sharpen resource allocation of their engineering talent, for instance, re-allocate their engineering resources away from pet single node projects that nobody uses like ATOM towards fixing the aforementioned issues with composability of inference optimizations between disaggregated inferencing, wide expert parallelism and FP4. The current subpar software is due to lack of focus and incorrect prioritization of where the industry already is at. All top tier labs are already using disaggregated inferencing and wide expert parallelism; AMD needs to stop focusing on single node and heavily invest focus into multi node inferencing for open source solutions.

Evidence

AMD is more than six months behind on open source distributed inferencing and wide expert parallelism and FP4 composability as shown by Nvidia and SGLang team showing off their NVFP4 performance on DeepSeek six months ago

Context After

Source: SemiAnalysis InferenceX ↗

② Atomic Claim

AMD 在 open-source distributed inferencing、wide expert parallelism 與 FP4 composability 上落後超過六個月；Nvidia 與 SGLang 團隊早在六個月前就已展示 DeepSeek 上的 NVFP4 效能。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "attribute": "COUNT",
  "context_nodes": [
    {
      "id": "04_knowledge_base/Expert Parallelism",
      "label": "wide expert parallelism"
    },
    {
      "id": "04_knowledge_base/FP4",
      "label": "FP4"
    },
    {
      "id": "02_companies/NVDA",
      "label": "Nvidia"
    },
    {
      "id": "04_knowledge_base/SGLang",
      "label": "SGLang"
    },
    {
      "id": "02_companies/DeepSeek",
      "label": "DeepSeek"
    },
    {
      "id": "04_knowledge_base/NVFP4",
      "label": "NVFP4"
    }
  ],
  "entity": {
    "id": "02_companies/AMD",
    "label": "AMD"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "AMD 在 open-source distributed inferencing、wide expert parallelism 與 FP4 composability 上落後超過六個月；Nvidia 與 SGLang 團隊早在六個月前就已展示 DeepSeek 上的 NVFP4 效能。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	AMD	AMD
context_0	wide expert parallelism	04_knowledge_base/Expert Parallelism
context_1	FP4	FP4
context_2	Nvidia	NVDA
context_3	SGLang	SGLang
context_4	DeepSeek	DeepSeek
context_5	NVFP4	NVFP4

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。