clm_da29ca2df0674ff8ad7912376ce03bf7_r1

IX2-0308

① SA Source

• Source: 開啟完整 SA 文章
• Section: AMD ATOM Engine
• Line hint: 500

Context Before

Furthermore, maintainers of open-source inference engines like vLLM are disappointed in AMD due to a lack of engineering and GPU resources provided by AMD. For example, Simon Mo, lead vLLM maintainer, states in this GitHub RFC that there is still no working MI355X that he can add to vLLM CI, hence the poor user experience. There are currently zero Mi355X tests on vLLM, while NVIDIA’s B200 has many tests on vLLM. Similarly, there are still not enough MI300X CI machines on vLLM. Upstream vLLM needs at least 20 more MI300 machines, 20 more MI325 machines and 20 more MI355X machines to reach the same level of usability as CUDA.

We at SemiAnalysis have been trying to get AMD to contribute more compute to vLLM and have had some success on that within the couple weeks. vLLM will start to get a couple of MI355X machines such that they can bring their CI test parity from 0% to non-0%. We will talk more about AMD’s previous lackluster contribution towards vLLM, SGLang, PyTorch CI machine situation & how Anush started to fix it in our upcoming State of AMD article. At SemiAnalysis, we will have internal dashboard to track the # of tests & quality of tests that AMD & NVIDIA runs on vLLM, SGLang, PyTorch, & JAX.

Evidence

This huge disparity in time to market continues to lead to ROCm lagging behind and leaving a huge opening for Nvidia to continue to charge an insane 75% gross margin (4x markup on cost of goods)

Context After

Source: Github ↗

② Atomic Claim

這種 time-to-market 的巨大差距持續讓 ROCm 落後，也讓 Nvidia 有空間維持約 75% 的高毛利率，相當於對 COGS 約 4 倍加價。

• Epistemic Mode: ASSERTED
• Mapping Status: COMPLETE

③ Semantic Frame

{
  "comparison_expression": "這種 time-to-market 的巨大差距持續讓 ROCm 落後，也讓 Nvidia 有空間維持約 75% 的高毛利率，相當於對 COGS 約 4 倍加價。",
  "entities": [
    {
      "id": "04_knowledge_base/ROCm",
      "label": "ROCm"
    },
    {
      "id": "02_companies/NVDA",
      "label": "Nvidia"
    }
  ],
  "frame_type": "COMPARISON",
  "metric": "MARGIN",
  "operator": "MULTIPLE_OF",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [
      "75%",
      "4"
    ],
    "temporal_mentions": []
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
comparison_entity_0	ROCm	ROCm
comparison_entity_1	Nvidia	NVDA

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。