Design
Design for Disassembly
AI prioritySpecify screw-based fastening, labeled subassemblies and tool-light access so servers, GPUs and cooling modules can be separated cleanly at end of life.
- Impact
- High
- Feasibility
- Medium
- AI relevance
- High
Reduce e-waste across the AI hardware lifecycle
8
strategies
Design
Specify screw-based fastening, labeled subassemblies and tool-light access so servers, GPUs and cooling modules can be separated cleanly at end of life.
Design
Decouple accelerators, memory and storage from the chassis so a node can be upgraded rather than replaced when a new AI hardware generation arrives.
Use
Redeploy retired training hardware into inference, staging or edge workloads, extending useful life by two to four years before recycling.
Procurement
Embed repairability, take-back guarantees, spare-part availability and recycled-content thresholds into every hardware tender.
End of life
Harvest DIMMs, SSDs, power supplies and interconnects from decommissioned nodes to feed spare-part pools and cut new procurement.
Operations
Convert air-cooled halls to liquid cooling with retrofit kits so rising AI rack density does not force premature demolition of infrastructure.
End of life
Route non-reusable hardware to certified recyclers capable of recovering copper, gold, palladium and rare-earth elements at high yield.
Operations
Track every asset from install to disposition with a digital passport recording composition, repairs and final treatment route.