Data center GPU spend is set to grow by USD 125.43 billion over 2026-2030, a 15.8% CAGR off a base that already had on-premises deployments valued at USD 59.05 billion in 2024 [S1]. The headline figure sits inside a wider GPU TAM sized at USD 102.8 billion in 2026 and a USD 652 billion endpoint for 2033, implying a 30.2% CAGR across consumer, mobile, embedded, and accelerator use cases combined [S3].
North America is the largest regional pool, taking 36.3% of incremental data center GPU value through 2030 [S1] and a 37% share of the total GPU market in 2026 [S3], with Asia Pacific flagged as the fastest-growing region across the 2026-2033 window on the back of Japan and South Korea AI and cloud build-outs [S3]. Training workloads held the largest 2024 revenue share inside the data center GPU segment, with on-premises the dominant deployment mode at the same baseline [S1].
Workload Mix Tilts From Training to Inference by 2027
Training was the single largest revenue line in 2024 across the data center GPU market, and the structural shift expected over the forecast is toward inference, which is projected to become the dominant AI workload by 2027 [S2]. AI workloads as a whole are projected to move from roughly 25% of total data center compute in 2025 to around 50% by 2030, with inference taking the leading role inside that mix [S2].
That transition matters for spec sheets: training clusters are tuned for high-bandwidth memory, large all-to-all fabrics, and very high power per rack, while inference fleets are designed for predictable latency, geographic distribution, and edge placement. The same data center GPU silicon can serve both, but the surrounding infrastructure decisions diverge, including cooling topology, PLC-driven facility controls, and pressure-sensor arrays on liquid manifolds. For buyers planning 2026-2028 deployments, the inference curve is the part of the demand picture most likely to drive multi-region footprint decisions.
Power and Density: From 162 kW/ft² to 176 kW/ft² by 2027
AI-focused facilities are pushing rack densities from a 2025 baseline of 162 kW per square foot toward 176 kW per square foot by 2027, a roughly 8.6% lift in two years that reflects the move from air-cooled general compute to liquid-cooled accelerator halls [S2]. US data center power capacity is projected to climb from about 30 GW in 2025 to 90 GW or more by 2030, implying a 22% annual growth rate in installed megawatts [S2].
Goldman Sachs Research sizes US data center demand growth at about 50% to 92 GW by 2027, equivalent to a 17% yearly rate between 2025 and 2028, with sensitivity bands of 20% upside if GPU power needs or customer demand run hot, and 14% downside if AI demand undershoots [S2]. Goldman also forecasts data center power consumption rising 165% from 2023 to 2030 across the global stack [S2]. For the cooling side of the bill of materials, flow-meter selection on coolant loops and pressure-transmitter ranges on chilled-water headers both shift upward as density rises, and the broader facility build-out is covered in detail in Data Center Cooling 2026: Sizing, Liquid Cooling Surge, and Spec Boundaries.
Investment Cycle: USD 6.7 Trillion Through 2030
Aggregate investment needed to meet compute demand through 2030 is sized at USD 6.7 trillion, framed as the largest infrastructure build-out in modern history, with USD 5.2 trillion of that directed at AI-ready data centers specifically [S2]. Roughly 100 GW of new data center capacity is expected to come online between 2025 and 2030, effectively doubling global capacity versus the 2023 baseline [S2].
Annual infrastructure spend is forecast to exceed USD 1 trillion per year by 2030, with AI workloads responsible for about 70% of that expansion [S2]. Inside the data center GPU segment, cloud deployment and on-premises are both expanding, with on-premises still the leading 2024 line at USD 59.05 billion and remaining a critical segment for buyers in finance and defense that need data sovereignty and lower inference latency on dedicated silicon [S1]. The same report frames a USD 170.14 billion market opportunity across 2025-2030 against the USD 125.43 billion incremental value line, suggesting vendor pipeline assumptions sit above the central CAGR case [S1].
Application Mix: Gaming Still Leads, AI/ML the Fastest-Growing Slice
Across the total GPU market, gaming is the largest 2026 application at 45% share, while AI and machine learning is the fastest-growing application through 2033 on enterprise and cloud adoption [S3]. That split is meaningful for procurement: gaming-led demand sustains consumer and prosumer discrete GPU volumes, while AI/ML growth drives the accelerator-grade HBM, NVLink-class fabrics, and high-TDP parts that show up in data center bills of materials.
Other tracked applications include video editing and 3D rendering, crypto mining and blockchain, research and academia, data science and analytics, and HPC, each with different unit-volume and average-selling-price profiles [S3]. Discrete GPUs, integrated GPUs, and virtual GPUs are the three product classes tracked across both hardware and software plus services revenue lines, with cloud-based deployment forecast to grow alongside on-premise for regulated and sovereign buyers [S3]. February 2026 activity in the regional build includes Yotta Data Services opening a USD 2 billion AI hub with NVIDIA GPUs in India, a concrete data point for the Asia Pacific acceleration thesis [S3].
Where Estimates Diverge: 15.8% vs 17% vs 30.2%
Three different growth rates appear across the source set, and they are not contradictions so much as different lenses on the same stack. Technavio's data center GPU-only number is 15.8% CAGR over 2025-2030, with a USD 125.43 billion incremental value and a 36.3% North America share of that increment [S1]. Avid Solutions' US data center demand figure is a 17% yearly rate between 2025 and 2028, with a 14% downside band and a 20% upside band depending on whether GPU power needs outrun supply or AI demand slips [S2].
Persistence Market Research frames the entire GPU market at 30.2% CAGR from 2026 to 2033, going from USD 102.8 billion to USD 652 billion, which mixes consumer gaming, mobile, embedded, and accelerator silicon into one line [S3]. The gap between 15.8% and 30.2% is mostly explained by scope: data center GPU spend is a subset of total GPU TAM, and the higher CAGR is also pulled up by faster-growing adjacent categories like AI/ML accelerators and integrated GPUs in edge devices. For industrial buyers, the practical read is that the data center slice is the slower-but-larger pool, while the broader market growth is led by the AI/ML application bucket.
Supply-Side Read-Across and Constraints
AI workloads consumed about 14% of global data center power in 2025 and are projected to reach 27% by 2027, more than doubling in two years [S2]. Infrastructure occupancy rates are expected to climb from 85% in 2023 to over 95% by late 2026, which compresses buyer lead time on capacity reservations and pushes more build-to-suit activity into the 2026-2028 window [S2]. US data center construction spend has roughly tripled in the three years through 2025, yet occupancy remains near record highs in most US markets [S2].
For process engineers and instrumentation buyers, the supply-side constraint that matters most is power, not silicon. Liquid cooling, industrial-valve selection on coolant manifolds, and servo-motor-driven CDU pumps are all scaling with the 162 to 176 kW/ft² density curve noted above. The same build cycle drives demand for facility-side controls, covered separately in SCADA Demand 2026-2030: Power, Cloud and Asia Lead the Spend Curve, and for plant-floor networking under Industrial Ethernet 2026: Market Sizing, Protocols, and Plant-Floor Cabling Specs.
The next data points to watch are the 2027 inflection where inference is expected to overtake training inside the AI workload mix, and the 2028 print on whether US data center capacity lands closer to the 14% downside band or the 20% upside band in the Goldman Sachs demand sensitivity [S2]. The February 2026 Yotta USD 2 billion AI hub opening is the first concrete Asia Pacific capacity data point in the source set and a useful template for sovereign-AI build-outs in India through 2030 [S3].