AlphaTON Capital Secures $82.5 Million Strategic GPU Infrastructure to Power Privacy-First Decentralized Artificial Intelligence Cluster after Successful Pilot Launch of Telegram's Cocoon AI

Core Insights - AlphaTON Capital Corp. has announced a strategic investment to acquire a high-performance compute cluster of over 1,000 Nvidia B200 GPUs, valued at approximately $68 million, positioning itself as a key compute provider for Telegram's Cocoon AI network [1][6][10] Transaction Structure and Financing - The acquisition will be financed through a combination of $30 million in equity capital from AlphaTON and $52.5 million in debt financing facilitated by Vertical Data, resulting in a total project capitalization of approximately $82.5 million [6][2] Strategic Infrastructure Partnership - AlphaTON has partnered with CUDO Compute, SNET Energy UK Ltd, and Vertical Data to deploy and operate the GPU cluster, ensuring technical excellence and sustainable power data center development [3][4] Hardware Specifications and Capabilities - The compute cluster will feature cutting-edge AI inference and training capabilities with a target utilization rate of 90%, generating revenue through dedicated workloads for Cocoon AI and external GPU rentals [5][7] Projected Financial Performance - The investment is projected to yield an internal rate of return (IRR) of 59.7%, a net present value (NPV) of $59.6 million, and a return on investment (ROI) of 615%, with total cash returned projected at $150 million over five years [7][8] Revenue Projections - Revenue projections are based on conservative assumptions, including a 20% annual price compression in the first year and a 10% compression in subsequent years, with consistent EBITDA margins ranging from 64% to 73% over a five-year forecast [8] Successful Pilot Program - AlphaTON successfully launched a pilot program for Cocoon AI on November 25, 2025, validating the technology and enhancing the network's scalability and performance [8][10] Strategic Rationale - The company is committed to an "AI For Good" initiative, promoting ethical access and user data sovereignty, positioning itself as a decentralized alternative to centralized AI services [9][10] Market Context and Competitive Positioning - The investment aligns with a growing demand for AI infrastructure, with McKinsey projecting a 3.5x growth in data center AI workload capacity from 2025 to 2030, highlighting the potential for value capture in the sector [16] Deployment Timeline and Commercial Milestones - Key milestones include transaction closure in Q4 2025, hardware delivery and performance benchmarking in Q1 2026, and full implementation of Cocoon AI workloads by Q2 2026 [13][15]