The AI Rack Supply Chain Is Expanding Beyond GPUs

DIGITIMES reported on September 11, 2026, that suppliers of thermal management, power supply and rail kits, the three Taiwan categories it identifies as closest to the physical AI rack, booked combined August revenue of NT$121.48 billion (about US$3.86 billion), based on monthly filings with Taiwan’s Market Observation Post System. Growth rates differed sharply: rail kits rose 191.2% year over year, thermal 58.4% and power supply 32.5%. 

 

Beyond the GPU, Deployment Depends on the Whole Rack 

An accelerator delivers value only after the surrounding rack systems are ready to support it. That means the hardware must be installed, powered, cooled, and maintained as part of a complete deployment system. According to NVIDIA, its Vera Rubin MGX design uses a fully liquid-cooled compute tray with a printed circuit board midplane instead of cable connections, while the rack combines 45°C liquid cooling with a liquid-cooled busbar. TrendForce projected in August 2026 that liquid-cooling penetration among AI chips would rise from about 33% in 2025 to 53% in 2026, and it expects NVIDIA GB and VR rack-scale shipments to double this year. 

 

What the Numbers Signal, and Where They Stop 

DIGITIMES reads the spread in growth rates as a sign of where rack-scale deployment is tightest. In our analysis, the three categories map to three physical demands of a dense rack: heat has to leave, power has to arrive, and equipment has to be mounted and serviced. The fast growth in rail kits is notable. 

Caution is warranted, however. These categories may also include non-AI products, and growth rates do not show each category’s size or the source of its growth. The figures show where money is flowing, not why. 

Denser Servers Make Mechanical Design More Critical 

As density rises, chassis height and internal layout affect how well rack space is used, and installation time, serviceability and mechanical compatibility become deployment variables. According to SemiAnalysis, NVIDIA CEO Jensen Huang said at CES 2026 that the cable-free design cut compute-tray assembly time from about two hours to five minutes. 

 

Rails must carry heavier systems 

Taiwan News noted that heavier AI servers raise requirements for rails, which must carry greater loads while staying smooth and durable. Wider use of liquid cooling also reduces space inside cabinets, driving demand for thinner rail designs. 

 

Chassis and Slide Rails: The Mechanical Foundation of Deployment 

Hongguo Technology (Aeon AI Technology) lists 1U, 2U and 4U rackmount chassis, an ATX embedded chassis and slide rails on its website. In general terms, the 1U-to-4U range reflects a trade-off between rack density and room for expansion cards, storage and airflow. The ATX embedded chassis suits systems built around standard ATX-format boards. Slide rails secure a chassis in the rack and let it be extended for service. 

Our view is that as thermal and power architectures grow more complex, mechanical components are no longer end-of-project accessories. Load, depth, clearance and compatibility requirements vary by rack and platform, so chassis and rails are best evaluated alongside the rack design and validated early, which reduces surprises at installation and friction in later maintenance. 

The next phase of AI infrastructure growth will require not only more compute, but also the physical systems that make high-density compute deployable, serviceable and scalable. For Hongguo Technology, whose product lines center on chassis and slide rails, that makes early conversations with customers during rack design the practical approach. 

 

Contact Aeon AI Technology for more information about the company and its services.