Think AI provides a unified AI supercompute ecosystem called AI Fabric, centered on its AI SuperNode hardware that delivers up to 135% higher power density than traditional GPU servers while using the same silicon. The platform’s ILM system dynamically orchestrates workloads across GPUs using real‑time VRAM, model, and thermal telemetry, offering a one‑click interface for training, inference, and deployment. This enables AI labs and enterprise data‑science teams to run large‑scale models locally with full data sovereignty, high performance, and lower energy costs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and AI developers often rely on conventional GPU servers that deliver limited compute density and inefficient power usage, making large‑scale model training expensive and restricting the ability to run AI workloads locally with full control over data and security.
Solution
Think AI offers a unified AI supercompute ecosystem called AI Fabric, built around the AI SuperNode hardware that delivers substantially higher sustained compute and power density than standard GPU servers while using the same silicon. The platform includes the ILM system, a hardware‑aware orchestration layer that dynamically allocates workloads across GPUs based on real‑time VRAM, model, and thermal telemetry, providing a one‑click experience for training, inference, and deployment. By consolidating office‑day compute with night‑time supercomputing, Think AI enables organizations to maintain AI sovereignty—running models locally with full privacy, security, and control—without sacrificing performance or incurring excessive energy costs.
Target Audience
Primary customers are AI research labs, enterprise data science teams, and technology firms that require high‑performance, cost‑effective compute for large‑scale model development while retaining full control over their data and infrastructure.
Features
- AI SuperNode delivers up to 135 % greater power density than comparable enterprise GPU servers while maintaining identical GPU count and silicon
- ILM system dynamically schedules workloads across GPUs using live telemetry for optimal VRAM, thermal, and model utilization
- Unified platform supports model training, inference, and deployment through a single, click‑driven interface
- Seamless transition from office‑day compute to night‑time supercompute cluster, maximizing hardware utilization
- Hardware‑aware architecture ensures consistent performance and energy efficiency across diverse AI workloads