General Compute builds and operates inference-first GPU campuses in power-advantaged regions—delivering fast deployment, low cost-per-token, and predictable performance for large-scale AI workloads.
As foundation models move from experimentation to production, inference becomes the dominant driver of AI compute. Inference workloads are persistent, energy-intensive, and highly sensitive to power costs and deployment timelines.
Inference compute demand is compounding faster than traditional data center capacity.
GPUs and future application-specific silicon are increasingly optimized around revenue per watt.
U.S. and EU grid constraints slow multi-hundred-MW deployments, even when capital is available.
General Compute is designed for this reality: energy-anchored, inference-first infrastructure.
Cheap, Stable Renewable Power
We secure long-term access to undervalued, mostly hydro power in grids with structural surplus, starting with Paraguay.
High-Density GPU Pods
We deploy modular, high-density, liquid-cooled GPU pods with <1.1 PUE and a 10-month NTP-to-COD cycle for 100 MW-class projects.
APIs, Routing, Billing & Observability
We operate an inference-optimized software stack that routes workloads across clusters, maximizes utilization and token-per-watt, exposes elastic capacity via APIs, and provides monitoring, billing and multi-tenant controls.
Second-cheapest grid-connected renewable energy globally, ≈$0.039/kWh.
100% renewable hydro with large exportable surplus.
Government motivated to attract AI infrastructure investment with clear paths for power allocation and industrial development.
Favorable tax treatment for export-linked services (1% regime).
Existing fiber backbone with 20–30 ms latency to major LATAM POPs.
$0.039
per kWh
100%
Renewable