{
 "id": "3.01",
 "slug": "scale-up-fabric-gpu-to-gpu-inside-the-rack-pod",
 "url": "/parts/networking-and-interconnect/scale-up-fabric-gpu-to-gpu-inside-the-rack-pod",
 "name": "Scale-up fabric",
 "qualifiers": "GPU-to-GPU inside the rack/pod",
 "layer": {
  "id": 3,
  "name": "Networking & interconnect"
 },
 "parent_id": null,
 "children": [],
 "summary": "The fabric linking GPUs to each other inside a single rack or pod, separate from the switching fabric that connects racks to each other.",
 "description_md": "Scale-up fabric is the shortest, highest-bandwidth network in the data center: it lets GPUs inside the same rack or pod address each other's memory directly, rather than going through the scale-out switch fabric (3.02, 3.03). It sits upstream of the copper spine and backplane cartridges (3.10) that physically wire it together and, at higher speeds, of the optics and co-packaged approaches (3.04, 3.08) being evaluated as a copper replacement. AI training and inference workloads that split a single model across many accelerators depend on this fabric scaling in lockstep with GPU count, since it sets the ceiling on how large a tightly coupled GPU domain can get. An investor should watch whether a proprietary approach keeps its lead over open alternatives being pushed by a wider industry consortium, since that split determines how concentrated the supplier base for switch chips, cables, and connectors stays as pod sizes grow.",
 "ai_delta": "Larger AI models split across more GPUs per training or inference job, which raises the bandwidth and reach demanded of the scale-up fabric linking them inside a rack or pod.",
 "bottleneck_status": null,
 "scale_tiers": [
  "hyperscale_building",
  "ai_campus",
  "gw_class"
 ],
 "density_modes": [
  "any"
 ],
 "scale_notes": "Present wherever multi-GPU racks or pods exist; the fabric's reach and topology (single rack vs. multi-rack pod) scale with cluster size rather than being an enterprise feature.",
 "flow": [
  "data"
 ],
 "companies": [
  {
   "company_id": "alphabet",
   "name": "Alphabet (Google)",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "GOOGL",
   "product_note": "ICI",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "amd",
   "name": "AMD",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "AMD",
   "product_note": "Infinity Fabric",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "astera-labs",
   "name": "Astera Labs",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "ALAB",
   "product_note": "Scorpio X",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "broadcom",
   "name": "Broadcom",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "AVGO",
   "product_note": null,
   "note": "scale-up Ethernet (Tomahawk Ultra)",
   "source_url": null
  },
  {
   "company_id": "nvidia",
   "name": "Nvidia",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "NVDA",
   "product_note": "NVLink/NVSwitch",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "ualink-consortium",
   "name": "UALink consortium",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "cooperative",
   "ticker": null,
   "product_note": null,
   "note": "members: AMD, Broadcom, Astera, Marvell, Cisco, hyperscalers",
   "source_url": null
  }
 ]
}