clm_875bf476e4094cc28d73d9ce8e96f835_r1

IX2-0013

① SA Source

• Source: 開啟完整 SA 文章
• Section: Introduction
• Line hint: 27

Context Before

InferenceXv2 builds on this foundation. It expands coverage to include large scale DeepSeek MoE disaggregated inference (disagg prefill, or simply “disagg”) with wide expert parallelism (wideEP) optimization to **all 6 NVIDIA western GPU SKUs from the past 4 years **as well as to every single AMD western GPU SKU released in the past 3 years – in total InferenceXv2 utilizes close to 1000 frontier GPUs for a full benchmark run across all SKUs.

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 ↗.

Evidence

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.

Context After

We will add DeepSeekv4 and other popular Chinese frontier models with day 0 support as over the past 6 months, we now have cleaned up a lot of tech debt and are able to move fast with stable infrastructure ↗. We will also be adding TPUv7 Ironwood and Trainium3 to InferenceX later this year! If you want to contribute to our impactful mission while earning a competitive compensation, consider applying here ↗.

② Atomic Claim

inferencex 也提供免費 data visualizer，讓 ML 社群可自行探索完整 dataset。

• Epistemic Mode: ASSERTED
• Mapping Status: PARTIAL

③ Semantic Frame

{
  "attribute": "UNSPECIFIED_ATTRIBUTE",
  "context_nodes": [],
  "entity": {
    "id": "04_knowledge_base/InferenceX",
    "label": "inferencex"
  },
  "frame_type": "ATTRIBUTE",
  "qualifiers": {
    "condition_text": null,
    "numeric_mentions": [],
    "temporal_mentions": []
  },
  "value": {
    "numeric_mentions": [],
    "value_text": "inferencex 也提供免費 data visualizer，讓 ML 社群可自行探索完整 dataset。"
  }
}

④ Canonical Entity Mapping

Role	Surface Label	Canonical Target
entity	inferencex	InferenceX

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。