WoolyAI Acceleration Service
GPU service with GPU core and memory resources used billing
Listed in categories:
Developer ToolsData ScienceSaaS
Description
WoolyStack is a revolutionary CUDA abstraction layer designed for GPU workload execution, enabling unprecedented efficiency and seamless integration for AI infrastructure management. It allows users to run Pytorch applications in a GPUless client environment, utilizing only CPU resources while maintaining high performance and scalability. With WoolyAI Acceleration Service, users can leverage actual GPU resources based on consumption rather than time, ensuring cost-effective and efficient GPU utilization.
How to use WoolyAI Acceleration Service?
To use WoolyStack, simply run your Pytorch application inside a Wooly Client container on your CPU infrastructure. The Wooly Runtime Library will handle the execution and resource management, allowing you to focus on your ML workloads without worrying about GPU hardware specifics.
Core features of WoolyAI Acceleration Service:
1️⃣
Decouples CUDA execution from GPUs for unbounded AI infrastructure management
2️⃣
Supports diverse GPU hardware and simplifies manageability
3️⃣
Enables isolated execution for enhanced privacy and security
4️⃣
Dynamic resource allocation and profiling for optimized performance
5️⃣
Billing based on actual GPU resources used, not time used.
Why could be used WoolyAI Acceleration Service?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Run Pytorch applications in a CPU-only environment using Wooly Runtime Library | ✅ | |
| # 2 | Utilize WoolyAI Acceleration Service for cost-effective GPU resource management | ✅ | |
| # 3 | Seamlessly integrate multiple ML workloads on shared GPU resources. | ✅ | |
Who developed WoolyAI Acceleration Service?
WoolyAI Inc. is dedicated to providing innovative solutions for AI infrastructure management, focusing on maximizing GPU utilization and simplifying the execution of machine learning workloads. Their technology is designed to enhance performance while reducing costs for users.