WoolyAI offers a cutting-edge AI infrastructure management system that decouples CUDA execution from GPUs, enabling unbounded management of AI workloads with a focus on efficiency and cost-effectiveness.
Features
CUDA Abstraction for Pytorch
Allows Pytorch apps to run in Linux containers on CPU-only infrastructure using the Wooly Runtime Library, effectively simulating CUDA environments without physical GPUs.
Actual GPU Resource Usage Billing
Bills users based on actual GPU cores and memory used rather than time spent, offering a more accurate and cost-effective billing method.
Dynamic Resource Allocation
Automatically scales GPU processing and memory according to the workload needs and provides dynamic allocation for enhanced performance and flexibility.
Multi Vendor GPU Hardware Support
Supports various types of GPU hardware, ensuring flexibility and compatibility across different environments and technological needs.
Isolated Execution for Privacy and Security
Provides an isolated execution environment to maintain data privacy and security, essential for sensitive AI workloads.
GPU Usage Credits
Accounts are credited with Wooly credits that are consumed based on GPU core processing and memory usage, allowing users to pay for the actual resources utilized during model execution.
PyTorch Application Support
Allows users to run their PyTorch applications in a Wooly Client container, leveraging CPU infrastructure for better performance.