GR1D delivers fast, reliable inference infrastructure — from land and power to GPU-ready compute. Our first 20 MW facility has live power, with 600 MW in the pipeline. You focus on the model. We handle everything else.
Turnkey data center construction — 20 MW to 1 GW+
GPU colocation from $135/kW/month
Dedicated GPU rental for AI inference & training
GR1D controls every layer of the stack — from land and power to substation and compute — to deliver AI data centers faster and cheaper than anyone else.
Acquire undervalued land in strategic locations with pre-secured transmission access across Texas. Two sites in South Houston plus East Austin and North Texas.
Secure long-term power contracts at $0.04/kWh, bypassing volatile energy markets.
Modular builds hit <12 months to energize—while competitors wait years for grid connections. 20 MW powered on in under 30 days.
Direct OEM procurement fortifies the balance sheet with appreciating infrastructure assets. Control the inputs, compress the timeline.
Energy demand is surging while HPC infrastructure lags behind. GR1D is positioned at the intersection of this massive supply-demand gap.
Global AI infrastructure spending is projected to exceed $500B by 2028. Every major cloud provider, sovereign government, and enterprise is racing to secure GPU capacity.
Demand for high-performance compute is outpacing available data center capacity by 3-5x. New facilities can't be built fast enough to meet the wave of AI training and inference workloads arriving by 2027.
Grid interconnection wait times now exceed 3-5 years in most U.S. markets. GR1D's Texas-first strategy bypasses this bottleneck with pre-secured transmission access and <12-month energization timelines.
As AI models move from training to production, inference workloads are exploding — requiring distributed, low-latency compute at scale. GR1D's modular architecture is built for this shift.
From first power-on to a 600 MW pipeline — GR1D is executing at speed.
The old model loses value over time
A model where infrastructure appreciates
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