Views: 0 Author: Site Editor Publish Time: 2026-09-11 Origin: Site
The rapid expansion of artificial intelligence is changing more than the chips inside data centers. It is also changing the physical design of the servers that contain, power and connect those components.
Traditional enterprise servers were generally designed around relatively standardized CPU platforms, storage devices and networking hardware. AI infrastructure is different.
Modern AI systems increasingly combine GPUs, custom accelerators, high-speed networking, large memory configurations and specialized cooling and power systems. This creates much greater variation in the physical architecture of server hardware.
Recent industry developments illustrate this shift.
In September 2026, AI chip company d-Matrix announced that it would use NVIDIA's NVLink Fusion technology to integrate its custom inference processors into NVIDIA data-center systems. The company expects systems using this architecture to become available in 2027.
Cerebras has also introduced new AI server hardware designed specifically for AI inference workloads, using a modular system architecture rather than simply adapting a conventional server platform.
These developments point to an important trend:
AI server hardware is becoming increasingly specialized.
And when the internal hardware changes, the enclosure often has to change with it.
A conventional rack server can often rely on relatively established mechanical dimensions.
AI servers are more complicated.
Depending on the architecture, a system may need to accommodate:
Multiple GPUs or AI accelerators
Large power supplies
High-speed networking hardware
Additional memory modules
High-speed interconnects
Storage devices
Cooling assemblies
Internal cable systems
Custom mounting structures
The mechanical enclosure therefore has to be designed around the actual hardware configuration rather than simply selecting a standard box.
For an AI hardware manufacturer, this creates an important design question:
How can the chassis accommodate complex internal hardware while remaining manufacturable, serviceable and scalable for production?
This is where custom sheet metal design becomes important.
One of the most important considerations when developing an AI server chassis is the internal layout.
The enclosure should not be designed independently from the components.
The position of GPUs, power supplies, storage devices, fans, cooling assemblies and cables directly affects the chassis structure.
For example, GPU cards may require specific clearance around connectors and cooling areas. Power supplies may require dedicated mounting structures. High-speed cables may also require controlled routing paths to avoid interference with other components.
For this reason, enclosure design should begin with the actual component layout or CAD information whenever possible.
A sheet metal manufacturer involved at an early stage can help identify potential mechanical conflicts before the design reaches production.
High-performance GPU and accelerator cards can be significantly larger and heavier than conventional expansion cards.
This means the chassis may require additional support structures.
Depending on the design, manufacturers may need:
GPU support brackets
Reinforcement panels
Internal mounting rails
Card guides
Adjustable support structures
Custom brackets
These components may appear simple, but poor positioning can create installation difficulties or mechanical stress.
For custom AI server chassis, the enclosure should therefore be designed around the actual accelerator dimensions, mounting points and service requirements.
Cooling is another major consideration.
AI workloads are increasing rack power density, creating additional thermal challenges for data center infrastructure. Industry discussions in 2026 have increasingly focused on high-density racks and the physical limitations of conventional cooling approaches.
Not every AI server uses the same cooling architecture.
Depending on the system, manufacturers may use:
High-airflow cooling
Dedicated fan assemblies
Hybrid cooling
Liquid-cooled components
Specialized heatsink structures
This affects the mechanical design of the enclosure.
Airflow openings, fan mounting positions, removable panels and internal partitions may all need to be integrated into the sheet metal structure.
For liquid-cooled systems, the enclosure may also need to accommodate piping connections, manifolds, pumps or other cooling-related structures.
The important point is that cooling should be considered during chassis development rather than added after the enclosure is finished.
AI systems can contain a large number of high-speed connections.
Poor cable routing can create several practical problems:
Difficult installation
Difficult maintenance
Restricted airflow
Connector interference
Excessive bending
Longer assembly time
A custom server chassis can incorporate cable-routing features directly into the sheet metal design.
Examples include:
Cable openings
Routing channels
Retaining brackets
Cable management panels
Removable access panels
Internal supports
These details can make a significant difference during final assembly.
AI servers are expensive and complex systems.
Components may need to be replaced or upgraded during their service life.
For that reason, service access should be considered during the initial mechanical design.
Instead of making the entire chassis difficult to disassemble, designers can use:
Removable side panels
Removable top covers
Service doors
Separate access panels
Captive hardware
Modular internal brackets
The objective is simple:
Allow technicians to reach critical components without unnecessary disassembly.
For manufacturers developing their own AI hardware platform, this can also make future product revisions easier.
A custom AI server chassis is not only an engineering problem.
It is also a manufacturing problem.
Two designs may have similar functions but very different production costs.
Factors such as:
Number of parts
Bend complexity
Number of welds
Material thickness
Hardware quantity
Surface treatment
Assembly time
Inspection requirements
can all affect the final cost.
For example, unnecessary welded structures may increase production time, while poorly planned bends can increase tooling or processing requirements.
A manufacturability review before production can identify these issues early.
AI hardware companies often develop products rapidly.
The mechanical design may continue changing while the electronics and computing architecture are being finalized.
This creates a need for manufacturing partners who can support more than simple production based on a finished drawing.
An ODM sheet metal partner can become involved during the transition from:
Concept → CAD → DFM Review → Prototype → Testing → Production
During this process, the manufacturer can review:
Sheet metal structure
Bend feasibility
Part segmentation
Hardware selection
Welding requirements
Assembly method
Surface finishing
Production scalability
This can help reduce the gap between an engineering prototype and a production-ready enclosure.
The requirements for a prototype are not always identical to those for mass production.
An AI server manufacturer may initially require only a small number of chassis for:
Engineering validation
Thermal testing
Hardware integration
Customer demonstrations
System testing
Once the design is validated, production volumes can increase.
The enclosure supplier therefore needs to understand how the design will transition from prototype to repeat production.
A good chassis design should not only work as a prototype.
It should also be practical to manufacture repeatedly.
This is why early DFM review can be valuable for AI server manufacturers developing a new product.
Before sending an AI server chassis into production, manufacturers should evaluate several questions:
The mechanical structure should be based on real component dimensions and mounting requirements.
Modular structures may make future revisions easier.
Airflow and cooling components should not be treated as an afterthought.
Serviceability should be considered during mechanical design.
A visually attractive prototype may still be expensive or difficult to manufacture.
A supplier that can support both stages can reduce the need to redesign the enclosure when production begins.
The growth of AI infrastructure is creating demand for increasingly specialized computing hardware.
Recent developments in custom AI processors and specialized server systems suggest that future data center hardware will not necessarily follow one universal server architecture.
For equipment manufacturers, this means the mechanical enclosure needs to evolve alongside the computing platform.
Custom sheet metal fabrication can provide a practical way to develop:
AI server chassis
GPU server enclosures
Rackmount chassis
Internal sheet metal brackets
Custom mounting panels
Server structural components
Power equipment enclosures
Other data center hardware structures
The key is not simply producing metal parts.
It is developing a chassis that works with the customer's hardware, assembly process and production requirements.
If you are developing an AI server, GPU server or other data center hardware and need a custom metal chassis, the enclosure can be reviewed from the early design stage.
Provide your CAD drawing, 3D model, dimensions or initial enclosure concept for an ODM-oriented manufacturability review.
The earlier the mechanical design is reviewed, the easier it can be to identify potential issues with bending, assembly, mounting, service access and production scalability before volume manufacturing.
Looking for a custom AI server chassis or GPU server enclosure? Send your design requirements for an ODM review.
An AI server chassis is the mechanical enclosure designed to house GPUs, AI accelerators, processors, memory, power supplies, storage and related hardware used for AI computing.
Yes. AI server chassis can be customized around specific GPU configurations, rack dimensions, power supplies, cooling systems, internal mounting requirements and service access.
Sheet metal such as steel, galvanized sheet and aluminum can be used depending on structural, weight, thermal and finishing requirements.
Yes. A capable ODM manufacturing partner can support prototype development, DFM review and transition toward repeat production.
A 3D CAD model, 2D drawing, dimensions or even an initial concept can provide a starting point for mechanical and manufacturability review.
