Auddia Inc. has detailed LT350, a distributed AI compute business positioned as a core asset in its proposed merger with Thramann Holdings. LT350 represents a patented infrastructure model that deploys AI data centers in parking lots without consuming parking spaces, addressing GPU underutilization and grid-constrained deployment challenges. The technology integrates modular GPU, memory, and battery cartridges into proprietary solar parking-lot canopies, creating what the company describes as a defensible platform for distributed AI inference.
According to Auddia, LT350 builds the distributed inference layer to complement hyperscalers' training infrastructure, aiming for faster deployment, lower operational costs, and greater energy efficiency while generating premium revenue. The architecture targets two urgent constraints in AI infrastructure: GPU underutilization and grid-constrained datacenter deployment. Jeff Thramann, CEO of Auddia and founder of LT350, stated that the solution addresses latency, power, and land constraints that define the next decade of AI infrastructure.
The company believes LT350 creates advantages by placing compute physically close to data sources, reducing dependence on strained electrical grids, enabling faster deployment, providing cost predictability, and aligning with data sovereignty requirements. Target verticals include hospitals requiring HIPAA-aligned inference, financial institutions needing low-latency execution, defense organizations with strict isolation requirements, biotech campuses running sensitive workloads, and autonomous-vehicle fleets needing local data processing.
LT350's power-sovereign architecture integrates solar generation and battery storage into each canopy, enabling behind-the-meter power buffering, peak-shaving, curtailment resilience, reduced interconnection requirements, and predictable long-term power economics. This design aims to scale despite mounting grid constraints facing utilities and hyperscalers. Parking-lot deployment offers zero land acquisition costs, preservation of parking functionality, and faster deployment with minimized zoning and permitting hurdles compared to traditional data centers.
The economic model combines modular GPU deployment, solar-plus-storage energy systems, and parking-lot-based data centers to deliver higher GPU utilization, higher revenue from premium inference services, lower energy costs, reduced grid impact, faster deployment, and improved resilience through distributed networks. LT350 accounts for approximately 50% of McCarthy Finney's $250 million discounted cash flow valuation. The technology is protected by 13 issued and 3 pending patents. For information about LT350, please visit www.LT350.com.
Forward-looking statements in the announcement involve risks and uncertainties, including conditions to closing the proposed merger, financing uncertainties, timing of consummation, and other factors detailed in SEC filings. Investors can find additional information through the SEC website at www.sec.gov or Auddia's investor relations resources. The proposed transaction would combine LT350 with Auddia under the new McCarthy Finney holding company if completed.
