GB2-0023
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Networking - Line hint:
259
Context Before
Out of Band Networking
Evidence
As a quick refresher, the frontend networking is just your normal ethernet network that you use to connect to the internet, SLURM/Kubernetes, networked storage, data loading, model checkpoints. This network is typically 25-50Gb/s per GPU, so on a HGX H100 server, it will be 200-400Gb/s per server, while on a GB200 computer tray node, it will be 200-800Gb/s per server depending on the configuration.
Context After
Your backend network is used to scale out GPU-GPU communications across hundred to thousands of racks. This network could either be Nvidia’s Infiniband or Nvidia Spectrum-X Ethernet or Broadcom Ethernet. With the options from Nvidia being way more expensive compared to the Broadcom Ethernet solutions ↗.
The scale-up accelerator interconnect (NVLink on Nvidia ↗, Infinity Fabric/UALink on AMD, ICI on Google TPU ↗, NeuronLink on Amazon Trainium 2) is an ultra-high speed network that connects GPUs together within a system. On Hopper, this network connected 8 GPUs together at 450GB/s each while on Blackwell NVL72, it will connect 72 GPUs together at 900GB/ each. There is a variant of Blackwell called NVL576 that will connect 576 GPUs together but basically no customers will opt for it. In general, your accelerator interconnect is 8-10x faster than your backend networking.
② Atomic Claim
GB200 compute tray node 的 frontend networking 依配置約為 200–800Gb/s per server。
- Epistemic Mode:
ASSERTED - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "frontend_network_bandwidth",
"context_nodes": [
{
"id": "04_knowledge_base/Ethernet",
"label": "Ethernet"
}
],
"entity": {
"id": "04_knowledge_base/GB200",
"label": "GB200"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [
"200-800Gb/s"
],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [
"200-800Gb/s"
],
"value_text": "200–800Gb/s per server depending on configuration"
}
}④ Canonical Entity Mapping
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。