TAHO offers a decentralized execution platform that taps into a global marketplace of idle CPUs, GPUs, and accelerators, letting AI developers and HPC researchers run workloads faster and cheaper. Through an intelligent scheduler and unified API, jobs are automatically matched, provisioned, and scaled across distributed nodes, providing low‑latency, cost‑effective compute close to data while maintaining security and real‑time performance analytics.
Funding
Funding not disclosed
Founders
Product
Problem
AI and high-performance computing (HPC) workloads often rely on centralized cloud providers, leading to underutilized hardware, high latency, and elevated costs, especially for bursty or geographically distributed tasks.
Solution
Taho provides a decentralized execution platform that aggregates idle compute resources across a global network, allowing users to run AI and HPC jobs on underused hardware. By routing workloads to the most suitable nodes, the platform reduces execution time and cost while enabling placement of compute close to data sources. Users submit jobs through a unified API, and Taho’s scheduler automatically provisions, monitors, and scales resources across the network. Results are returned securely, and the system tracks resource utilization to minimize waste and improve overall efficiency.
Target Audience
Primary customers are AI developers, data scientists, and HPC researchers in enterprises or research institutions that need scalable, cost-effective compute resources for training models or running large simulations.
Features
- Global marketplace of idle CPUs, GPUs, and specialized accelerators accessible via a single API
- Intelligent scheduler that matches workloads to optimal nodes based on latency, cost, and hardware requirements
- Automatic scaling and fault tolerance across distributed nodes to maintain job continuity
- End-to-end encryption and sandboxed execution environments for data security
- Real-time performance analytics and cost reporting dashboards for users
- Compatibility with popular AI frameworks (TensorFlow, PyTorch) and HPC job schedulers (SLURM, PBS)