Overview
Server and GPU embodied carbon is one of the largest data points in any hyperscaler's Scope 3 inventory, and yet it's where spend-based emission factors fail most catastrophically. EPA NAICS supply-chain factors for semiconductor manufacturing estimate a data center GPU at roughly 7,500 kg CO2e. Published product carbon footprints from primary LCA data put the number closer to 160. That's a 45x gap, baked into a lot of Scope 3 inventories right now.
REEL LCI provides server and GPU life cycle inventory data by aggregating its component-level datasets — chiplet GPU compute die, HBM stacks, silicon interposers, package substrates, high-layer-count PCBs, cabling, PSUs, thermal management, chassis — into card-level and server-level inventories. The result is a primary-data, bottom-up alternative to spend-based factors that produces results in the PCF range rather than the EEIO range.
The dataset is intended for hyperscalers, AI infrastructure vendors, and Scope 3 reporters tracking embodied impact of data center hardware procurement.
What's covered
The server/GPU branch of REEL LCI aggregates component-level datasets into system-level inventories:
- System-level components (24 models): GPU accelerator cards, server boards, networking modules, storage controllers — assembled from die + packaging + PCB + memory.
- Infrastructure (14 models): Power supply units (PSUs), server chassis, rack-level components.
- Cabling (10 models): Direct-attach copper (DAC) at 25G/100G, multi-mode and single-mode fiber (OM4, OS2), Cat-6A patch cables, server power cords.
- Connectors (10 models): Server-grade signal and power connectors.
- Thermal management (5 models): Heat sinks, cold plates, fan modules.
System-level models are aggregations — not standalone process flows — so the composition of each one is documented: a given GPU card lists its constituent compute die, HBM stacks, interposer, PCB, and chassis datasets. Where alternative configurations matter (different HBM generation, different PCB layer count, alternative GPU chiplet architecture), REEL publishes them as separate aggregation variants rather than offering parameter knobs on a single dataset.
Methodology
A GPU accelerator card model is built by aggregating its constituent components: a 3nm chiplet compute die (using the wafer + IC packaging datasets), HBM3 stacks (using the memory dataset), a silicon interposer or fan-out structure (using the advanced packaging dataset), a package substrate, a 12–16 layer high-performance PCB (using the PCB dataset), a heat sink (using the thermal dataset), DAC or fiber cabling (using the cable dataset), and a fraction of the chassis. Yields compound across the assembly steps: each component carries its own yield assumption, plus a system-level final-test yield.
This bottom-up approach means changing any underlying assumption — chiplet architecture, HBM stack height, PCB layer count, regional electricity grid — propagates through to the system-level inventory automatically. Use-phase impacts (electricity to run the server) are explicitly excluded; REEL is cradle-to-gate. The full methodology is documented in the REEL LCI Methodology Report v0.1 (PDF).
Related insights
- The Spend-Based GPU Gap — the full 45x gap analysis between EEIO-style spend factors and primary-data PCFs for data center GPUs.
- 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 component-level inventories that aggregate up to data center hardware: 24 system-level component models, 14 infrastructure models (PSUs, chassis, racks), 10 cable models (DAC, fiber, power), 10 connector models, and 5 thermal-management models. These combine with the wafer, packaging, PCB, and memory datasets to assemble GPU accelerator cards, server boards, and full server SKUs.
Spend-based emission factors (e.g., EPA NAICS 334413 at 0.215 kg CO2e/dollar) estimate a data center GPU at roughly 7,500 kg CO2e per H100-class card. Published product carbon footprints from primary LCA data (the WSP-NVIDIA HGX H100 baseboard PCF) put the actual number closer to 160 kg per card — a 45x gap. REEL LCI uses the same primary-data, bottom-up approach, so it produces results in the PCF range rather than the spend-based range.
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.
A GPU accelerator card model aggregates the chiplet compute die (3nm wafer + IC packaging), the HBM stacks (memory wafer + TSV packaging), the silicon interposer (CoWoS or similar), the package substrate, the PCB (typically 12–16 layer high-performance), the heat sink, the cabling, and the chassis fraction. Yields compound across the assembly steps — for example, the system-level yield includes the probability that all components on the assembled card pass final test.