clm_b7e05f9dc7c64329bde3784c53de0aeb_r1

IX2-0599

① SA Source

• Source: 開啟完整 SA 文章
• Section: Adding TPU, Trainium and More Models
• Line hint: 818

Context Before

Adding TPU, Trainium and More Models

Currently, we continuously benchmark DeepSeek R1 and GPT OSS 120B (previously Llama 3.1 70B as well). To keep up with the newest model architectures, we plan on adding DeepSeek V3.2 (w/ DSA), DeepSeek V4 on Day 0, Kimi K2.5, Qwen3, GLM5, and many more over the course of the next few months. We will also eventually add multi-modal models and be using EPD & CFD (invented by TogetherAI) optimization too.

Evidence

In addition to new models, we are actively working on adding both TPU and Trainium

Context After

Total Cost of Ownership (NVL72, Blackwell, Blackwell Ultra, MI355, Hopper, MI325, MI300)

Looking at capital costs across comparable generations, Nvidia systems tend to have higher capital cost than AMD systems. This is driven mostly by higher compute tray content which is driven by higher GPU pricing – it is well known from their financials that Nvidia enjoys higher margins on their GPUs than other vendors. As an example, MI300X compute tray content sits at ~$138Kversus$170K for H100 SXM, and the gap widens further in later generations. MI355X is at ~$197K,while[[04_{k}nowledg{e}_{b}ase/NVIDIAB200|B200]]risesto$264K and B300 to ~$344K. That incremental silicon content flows directly into higher server cost, and ultimately higher all-in cluster capex per server.

② Atomic Claim

除了新增 models，團隊也正積極加入 TPU 與 Trainium。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "UNSPECIFIED_ATTRIBUTE",
  "context_nodes": [
    {
      "id": "04_knowledge_base/Trainium",
      "label": "Trainium"
    }
  ],
  "entity": {
    "id": "04_knowledge_base/TPU",
    "label": "TPU"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "除了新增 models，團隊也正積極加入 TPU 與 Trainium。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	TPU	TPU
context_0	Trainium	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。