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Who makes merchant GPUs / accelerators for AI data centers

GPUs and other accelerator chips designed by merchant vendors and sold to any buyer, rather than designed in-house by a single hyperscaler; the workhorse silicon for AI training and inference.

Merchant accelerators are the chips that do the actual matrix math in AI training and inference — bought off a price list (allocation permitting) rather than designed for one company's fleet alone. They sit downstream of foundry (the advanced-node foundry step) and advanced packaging, which combine the compute die with HBM stacks onto a substrate, and upstream of the server board and tray assembly in Layer 4, where they're paired with server CPUs, NICs, and cooling. Because merchant GPUs are sold to every buyer from a single enterprise pilot through a gigawatt-class campus, this is the part of the catalog investors search first when asking who benefits from AI buildouts. An investor should watch supplier concentration — a small number of vendors set the pace for each new architecture generation — alongside qualification cycles, since a new generation typically requires re-validation across the software stack, board designs, and cooling before hyperscalers will deploy it at scale. Pricing has moved less on open competition than on allocation: demand has outrun available packaging and wafer capacity for multiple generations running, which has kept lead times elongated and kept the vendors that can secure capacity in a stronger negotiating position than a normal semiconductor upcycle would imply.

AI delta: AI training and inference workloads are the primary demand driver for merchant accelerators, which is why unit volumes and generational upgrade cadence have both accelerated well beyond a typical chip replacement cycle.

Per rack: 72 GPUs per NVL72 rack (Nvidia rack specification, via taxonomy v0.1 Appendix A). Per GW: ~800,000 GPUs per GW at one real facility (Nebius's Finland site: 60,000 H100-class GPUs in a 75 MW expansion, Nebius, 2024-10-08) -- not a fixed industry ratio: GPU count per MW moves with generation, power draw and cooling design, and no source found states a standardized per-GW figure.

Appears at:Enterprise / small coloLarge colo / hyperscale buildingAI campusGW-class campus· cooling:any — Same die designs ship into an enterprise pilot rack and a gigawatt campus alike; only unit count and generation mix change with scale.

Companies on this part (3)

CompanyRoleOwnershipTickerPrice1dConfidenceNote
AMDmanufacturerpublicAMD517 USD+2.2%disclosedInstinct MI350/MI400 · source
IntelmanufacturerpublicINTC86.96 USD+2.6%inferredGaudi 3
NvidiamanufacturerpublicNVDA211 USD-0.7%disclosedBlackwell Ultra · source

Who competes here

The same table read the other way: every company above competes for merchant GPUs / accelerators sockets. Confidence tags matter — an inferred placement is a lead, not a confirmed rivalry.

Seen in real machines

merchant GPUs / accelerators is named in 17 of the rack teardowns and supercomputer breakdowns on this site. Each line below is a piece of that machine. The companies shown are the ones named on that line, not necessarily the maker of this part.

Nearby parts

From the blog

Watch

Understanding NVIDIA GPU Hardware as a CUDA C Programmer | Episode 2: GPU Compute Architecture
Tushar Gautam — walks through the compute architecture inside a merchant Nvidia GPU (SMs, warps, memory hierarchy)
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Questions about merchant GPUs / accelerators

What are merchant GPUs / accelerators?
GPUs and other accelerator chips designed by merchant vendors and sold to any buyer, rather than designed in-house by a single hyperscaler; the workhorse silicon for AI training and inference.
Which companies make merchant GPUs / accelerators for AI data centers?
This catalog lists 3 companies on Merchant GPUs / accelerators, 3 of them with listed equity: AMD, Intel, Nvidia.
How does AI change demand for merchant GPUs / accelerators?
AI training and inference workloads are the primary demand driver for merchant accelerators, which is why unit volumes and generational upgrade cadence have both accelerated well beyond a typical chip replacement cycle.
How many merchant GPUs / accelerators does a data center need?
Per rack: 72 GPUs per NVL72 rack (Nvidia rack specification, via taxonomy v0.1 Appendix A). Per gigawatt of capacity: ~800,000 GPUs per GW at one real facility (Nebius's Finland site: 60,000 H100-class GPUs in a 75 MW expansion, Nebius, 2024-10-08) -- not a fixed industry ratio: GPU count per MW moves with generation, power draw and cooling design, and no source found states a standardized per-GW figure. Figures come from the taxonomy behind this catalog.
How reliable are these merchant GPUs / accelerators supplier placements?
The 3 placements on this part break down as 2 disclosed, 1 inferred. "disclosed" means the company or a filing says so; "reported" comes from trade press or a reference-design list; "inferred" is a taxonomy lead with no citation yet.

Sources: placements without a source URL come from the taxonomy seed and are tagged accordingly. How this data is built.· Markdown · JSON