WYFI Investor FAQ
A resource for investors to better understand WhiteFiber's mission, operations, and market opportunity in building high-performance infrastructure for generative AI.

Company & Business Overview
What is WhiteFiber?
What is WhiteFiber's mission?
What is your business model?
Who are your customers?
Where are your operations based?
What kinds of workloads are running on WhiteFiber infrastructure?
Where can I find equity research reports about WhiteFiber, Inc. and does WhiteFiber, Inc. provide copies of analyst research reports?
Market & Strategy
What is WhiteFiber’s market opportunity?
How does WhiteFiber differentiate from its competitors?
What is WhiteFiber’s growth strategy?
Stock & Investor Details
What exchange is WhiteFiber listed on, and what is the ticker symbol?
How can I purchase shares?
Who is WhiteFiber’s transfer agent?
Corporate Governance
Who are the members of WhiteFiber’s leadership team and board of directors?
What are WhiteFiber’s corporate governance policies?
Investor Communications
How can I receive investor updates?
Who do I contact for investor relations inquiries?
Glossary of Key Terms
Generative AI workloads
Tasks like training and running large AI models (e.g., ChatGPT, image generators) that require massive computing power.
GPU (Graphics Processing Unit)
Specialized computer chips originally built for graphics, now essential for AI because they can handle huge amounts of data in parallel. Think of them as the "engines" powering AI.
GPU Clusters
Groups of GPUs working together like a supercomputer to handle big AI jobs.
Cloud Services (GPU Cloud)
Renting or leasing access to GPUs over the internet instead of owning expensive hardware.
Colocation Services
Solutions offering access to space, power, cooling, and on-site support inside a third-party data center for operating hardware owned by the end customer.
Data Centers (Tier III)
Data Centers (Tier III) – Buildings filled with servers and networking equipment, engineered for reliability. "Tier III" aligns to requirements set forth by the Uptime Institute guaranteeing high uptime and resilience.
High Power Density / up to 150kW Racks
Each server rack can use up to 150,000 watts of power much higher than standard racksallowing for more GPUs in a smaller footprint and accommodating emerging hardware design with higher power requirements.
Direct-to-Chip Liquid Cooling
A cooling method that uses liquid instead of air, to support modern GPUs while improving efficiency and power consumption.
N+1 Redundancy
Backup systems for power/cooling; if one component fails, another immediately takes over so operations never stop.
Brownfield Retrofit
Converting an existing building into a data center.
Greenfield Build
Constructing a new data center from scratch.
Edge Locations
Smaller data centers placed closer to where data is being generated/used, reducing delays.
AI Training vs. Inference
Training: Teaching an AI model using large amounts of data.
Inference: Using the trained model to make predictions or generate output.
Large Language Models (LLMs)
AI systems trained on massive text datasets to understand and generate human-like language.
Vertical Integration
Owning the full chain of operations (power, data centers, cloud services), giving more control, lower costs, and faster delivery compared to companies that outsource.
Hyperscalers
Cloud providers like Amazon, Google, or Microsoft that run large scale, multi-purpose cloud services.