Overview
Server and accelerator hardware combines some of the most process-intensive parts of the electronics stack: advanced logic, HBM, silicon interposers, high-layer-count PCBs, power conversion, cooling hardware, high-speed interconnects, and rack infrastructure. Spend-based factors compress that physical system into a monetary proxy and cannot show which components drive the result.
REEL LCI provides the component and infrastructure inventories needed to build a transparent bottom-up model of that hardware. Practitioners select the relevant logic ICs, memory, packaging, substrates, PCBs, storage, power, thermal, cabling, and infrastructure datasets and combine them using the bill of materials for the product being studied.
The public catalog does not currently publish a complete populated GPU accelerator card or full server SKU. This page is an SEO guide to the REEL datasets used in those studies, intended for hyperscalers, AI infrastructure vendors, LCA practitioners, and Scope 3 teams.
What's covered
The catalog provides the following data-center hardware building blocks:
- Components (30 datasets): Memory modules, SSD and HDD storage, server and desktop PSUs, voltage-regulator modules, fan arrays, and other assembled electronics.
- Infrastructure (14 datasets): MGX tray and rack structures, busbars, power distribution, slide rails, manifolds, fan walls, and quick-disconnect assemblies.
- Cabling (10 datasets): Direct-attach copper at 25G/100G, multimode and single-mode fiber assemblies, Cat-6A, server power cords, and internal harnesses.
- Connectors (10 datasets): PCIe, DDR5, mezzanine, network, storage, fiber, USB4, and power connectors.
- Thermal and electromechanical (9 datasets): Heat sinks, heat pipes, vapor chambers, thermal interface materials, fans, blowers, and motors.
- Optoelectronics (12 datasets): Optical fibers, transceivers, LEDs, detectors, and other optical subassemblies used in data-center networks.
These sit alongside the wafer, logic-IC, memory, packaging, substrate, and PCB datasets needed for a complete hardware study. The exact system composition remains explicit rather than being hidden in a generic server average.
Browse the documentation for data-center component and infrastructure datasets.
Methodology
A bottom-up accelerator study starts with the product bill of materials. The compute device can be represented using the relevant wafer, logic-IC, and packaging datasets; HBM comes from the memory stack models; interposers and substrates come from advanced packaging; the board uses the matching 12–16 layer PCB construction and finish; and power, cooling, cabling, storage, tray, and rack inventories are added in their physical quantities.
This approach keeps architecture choices visible. HBM generation and stack height, package type, PCB layer count and finish, cooling design, power architecture, and rack allocation can all be changed without treating the server as a single opaque average. Use-phase impacts remain a separate study input. The full inventory methodology is documented in the REEL LCI Methodology Report v1.0 (PDF).
Related insights
- The Spend-Based GPU Gap – why monetary proxies and physical component models can produce very different hardware estimates.
- The Chiplet Tradeoff – why splitting GPUs into chiplets shifts carbon from die to package.
- The HBM Footprint Inversion – why HBM contributes disproportionately to GPU embodied carbon.
- The 40-Layer PCB Problem – why high-performance server PCBs scale super-linearly with layer count.
Frequently asked questions
REEL LCI provides the building blocks for bottom-up server and GPU studies: 30 component datasets, 14 infrastructure datasets, 10 cable datasets, 10 connector datasets, 9 thermal and electromechanical datasets, and 12 optoelectronics datasets, alongside logic IC, memory, packaging, PCB, and substrate inventories. The public catalog does not currently publish a complete populated GPU accelerator card or server SKU.
Spend-based factors collapse a complex hardware purchase into one monetary proxy. REEL's process- and component-level inventories let practitioners model the physical bill of materials instead, choosing the relevant logic, memory, packaging, PCB, power, cooling, cabling, and infrastructure datasets for the system being studied.
No. REEL LCI provides cradle-to-gate inventories: manufacturing impacts only. Use-phase electricity consumption depends on the workload, the data center's PUE, and the regional grid mix; practitioners add use-phase impacts on top of REEL's manufacturing inventories using their own utilization assumptions and grid-emission factors.
Practitioners can assemble a system model from the relevant REEL building blocks: logic IC and wafer fabrication, HBM stacks, advanced packaging and interposers, package substrates, high-layer-count PCBs, power conversion, cooling, cabling, and an allocated share of tray or rack infrastructure. The system bill of materials and final assembly assumptions remain explicit inputs to that study.