Enteligent Launches Modular Power Architecture for AI

The new three-stage system aims to streamline power conversion for high-density artificial intelligence data centers.

Updated on Oct. 26, 2026 in Data Centers

Isometric editorial illustration featuring a modular power conversion unit with metallic cooling fins and busbars in a muted, professional color palette. — Enteligent has launched its PowerCore architecture, a three-stage power conversion system designed to optimize energy efficiency for high-density AI data centers. AI Illustration. Upload story photo >

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Enteligent has introduced the PowerCore modular power stack, a three-stage conversion architecture designed to support the intense power demands of AI data centers. The system enables higher compute density by optimizing how power is converted from rack-level supply to GPU voltage rails.

Why it matters

AI data centers are increasingly constrained by rising power requirements and limited physical space for hardware. This new architecture helps operators manage higher thermal loads and power densities as machine-learning systems scale.

The PowerCore stack includes a 100 kW RackCore shelf that converts 800 VDC to a 50/48 VDC bus, feeding into a 16-phase ChipCore module to reach 1.2/1.7 VDC. This design boosts rack compute density by up to 50 percent while eliminating the need for separate battery backup shelves.

The players

Enteligent

Enteligent is a technology company focused on power conversion solutions for data centers and other high-demand environments.

The details

The architecture divides power conversion into three electrical domains, treating voltage, frequency, and electromagnetic emissions as a single integrated system. By moving from 800 VDC down to the final GPU rails, the system manages server-mounted power more efficiently than previous methods.

Enteligent launched the PowerCore modular power stack on October 26, 2026.
Machine-learning systems are projected to require 500 kW per IT rack by 2030.

The Tech Race

The transition to high-voltage distribution marks a shift from legacy power systems toward specialized infrastructure designed specifically for AI. This architecture competes with traditional conversion methods by replacing bulk battery shelves with highly integrated server-level modules.

As data centers adopt this technology, users may experience improved stability and performance for AI-driven cloud services. The architecture enables more compute power within the same footprint, helping to keep pace with the increasing demand for generative AI applications.

The takeaway

Data center operators should evaluate whether their current power infrastructure can handle the shift toward 500 kW per rack requirements by 2030. Integrating modular, high-efficiency conversion systems is a critical step for maintaining compute capacity in space-constrained facilities.