{
 "id": "1.02",
 "slug": "hyperscaler-custom-asics-xpus",
 "url": "/parts/compute-silicon-and-packaging/hyperscaler-custom-asics-xpus",
 "name": "Hyperscaler custom ASICs",
 "qualifiers": "XPUs",
 "layer": {
  "id": 1,
  "name": "Compute silicon & packaging"
 },
 "parent_id": null,
 "children": [],
 "summary": "AI accelerator chips (often called XPUs) designed in-house by a hyperscaler for its own fleet, typically co-developed with an outside ASIC design partner and fabbed by a merchant foundry.",
 "description_md": "Custom ASICs are a hyperscaler's answer to the cost and allocation constraints of merchant GPUs (Merchant GPUs / accelerators): a chip specified in-house, usually with an outside design-services partner (ASIC design services, IP & EDA) supplying implementation and IP blocks, then built through the same advanced-node foundry and advanced packaging supply chain as everyone else's accelerators. Because these parts are proprietary to one buyer, they don't compete on an open price list — they compete on whether a hyperscaler's own workload mix justifies the design cost of not buying merchant silicon.\nFor investors, the interesting exposure here usually isn't the hyperscaler (who doesn't sell the chip) but the design-services and IP partner, whose revenue scales with design starts and program renewals rather than with unit shipments. Supplier concentration is real: a small number of design houses handle most of the announced custom AI silicon programs, and qualification is effectively a multi-year, single-customer relationship rather than a repeatable sales motion, which makes program wins and losses unusually consequential events for the partner's business.",
 "ai_delta": "Hyperscalers commission custom ASICs specifically to cut the cost of AI training and inference at their own fleet scale, which is why this category exists at all outside of a few prior-generation exceptions.",
 "bottleneck_status": null,
 "scale_tiers": [
  "enterprise",
  "hyperscale_building",
  "ai_campus",
  "gw_class"
 ],
 "density_modes": [
  "any"
 ],
 "scale_notes": "A custom ASIc program only pencils out once a hyperscaler's own deployment volume is large enough to amortize the design cost, so presence skews toward hyperscale and campus-scale fleets even though the finished chips can run in smaller server pools too.",
 "flow": [
  "compute"
 ],
 "companies": [
  {
   "company_id": "alchip",
   "name": "Alchip",
   "role": "design_partner",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "3661.TW",
   "product_note": "AWS Trainium/Inferentia",
   "note": "partner on AWS Trainium/Inferentia",
   "source_url": null
  },
  {
   "company_id": "alphabet",
   "name": "Alphabet (Google)",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "GOOGL",
   "product_note": "TPU",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "amazon",
   "name": "Amazon (AWS)",
   "role": "in_house",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "AMZN",
   "product_note": "Trainium/Inferentia",
   "note": "Annapurna in-house",
   "source_url": null
  },
  {
   "company_id": "broadcom",
   "name": "Broadcom",
   "role": "design_partner",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "AVGO",
   "product_note": "Google TPU; Meta MTIA",
   "note": "partner on Google TPU; partner on Meta MTIA",
   "source_url": null
  },
  {
   "company_id": "marvell",
   "name": "Marvell",
   "role": "design_partner",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "MRVL",
   "product_note": "AWS Trainium/Inferentia; Microsoft Maia",
   "note": "partner on AWS Trainium/Inferentia; partner on Microsoft Maia",
   "source_url": null
  },
  {
   "company_id": "mediatek",
   "name": "MediaTek",
   "role": "design_partner",
   "confidence": "reported",
   "ownership_type": "public",
   "ticker": "2454.TW",
   "product_note": "Google TPU",
   "note": "partner on Google TPU (on later generations)",
   "source_url": null
  },
  {
   "company_id": "meta",
   "name": "Meta Platforms",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "META",
   "product_note": "MTIA",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "microsoft",
   "name": "Microsoft",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "MSFT",
   "product_note": "Maia",
   "note": null,
   "source_url": null
  },
  {
   "company_id": "openai",
   "name": "OpenAI",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "subsidiary",
   "ticker": null,
   "product_note": "custom accelerator",
   "note": null,
   "source_url": null
  }
 ]
}