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Who makes rack-scale systems for AI data centers

Rack-scale systems (NVL72-class, MI400 Helios-class)

A rack-scale system is the fully integrated cabinet of compute trays, switch trays, power shelves, cooling manifolds and busbar that ships as one wired unit rather than as discrete servers.

The rack-scale system is the top-level assembly layer: it takes the GPU baseboards (4.03), switch trays (4.04), power shelves (4.12) and liquid-cooling hardware from layer 5 and integrates them into a single cabinet wired for one scale-up fabric domain (layer 3). Nvidia and AMD publish the reference architecture and mechanical/electrical spec; the ODMs and OEMs listed against this part build to that spec, badge the result, and compete mainly on integration quality, lead time and support rather than on the underlying design. AI training and inference clusters push this part into existence at all: a conventional enterprise server rack has no equivalent, because the point of a rack-scale system is to turn dozens of GPUs into one coherent domain, something only large models require. For investors, the interesting split is between the reference-design owners (Nvidia, AMD), who capture the architecture value, and the ODM/OEM tier, which competes on manufacturing scale and qualification speed with each new rack generation. Each new generation resets the qualification race, so watch which ODMs get design wins early and which fall behind on ramp.

AI delta: AI training and inference workloads are the reason rack-scale systems exist at all — no other server class assembles dozens of GPUs into one wired, liquid-cooled domain.

Appears at:Large colo / hyperscale buildingAI campusGW-class campus· cooling:liquidhybrid — enterprise deployments typically use standalone or multi-GPU servers rather than a full rack-scale system; the fully wired cabinet becomes the norm once GPU counts justify NVLink or Infinity Fabric domains at hyperscale and above.

Companies on this part (23)

CompanyRoleOwnershipTickerPrice1dConfidenceNote
AMDdesignerpublicAMD477 USD-1.7%inferredAMD design arm; manufacturing → Sanmina SANM; 2025
ASRock Rackoempublic3515.TW219 TWD+0.5%inferred
ASUSoempublic2357.TW957 TWD-1.5%inferred
CiscooempublicCSCO112 USD-0.1%inferred
Compalodmpublic2324.TW40.10 TWD+1.1%inferred
DelloempublicDELL472 USD+0.0%inferred
Eviden/AtosoempublicATO.PA28.52 EUR0.0%inferred
Foxconn (Hon Hai)odmpublic2317.TW253 TWD+0.2%inferredIngrasys
Fujitsuoempublic6702.T3938 JPY+4.7%inferred
Gigabyteoempublic2376.TW348 TWD+0.4%inferred
H3C · unit of Unisplendouroemsubsidiary000938.SZ*inferred
HPEoempublicHPE54.41 USD-3.7%inferred
Inspuroempublic000977.SZ79.10 CNY+1.1%inferred
Inventecodmpublic2356.TW65.00 TWD-0.9%inferred
Lenovooempublic0992.HK30.84 HKD+0.4%inferred
MiTAC (Tyan)odmpublic3706.TW91.30 TWD-0.3%inferred
NECoempublic6701.T4955 JPY+5.2%inferred
NvidiadesignerpublicNVDA228 USD+3.8%inferred
Pegatronodmpublic4938.TW88.80 TWD-0.7%inferred
Quanta Computerodmpublic2382.TW332 TWD-0.1%inferred
SupermicrooempublicSMCI38.46 USD+0.3%inferred
Wiwynn · unit of Wistronodmpublic6669.TW7020 TWD+3.5%inferred
xFusionoemprivateinferred

Who competes here

The same table read the other way: every company above competes for rack-scale systems sockets. Confidence tags matter — an inferred placement is a lead, not a confirmed rivalry.

Nearby parts

Watch

NVIDIA GB200 NVL72 | ASUS AI POD
ASUS — vendor walkthrough of an NVL72-class rack-scale system build
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Sources: placements without a source URL come from the taxonomy seed and are tagged accordingly. How this data is builtMarkdown · JSON