Unprecedented Efficiency
Reimagined Consumption
Diverse GPU Support
Seamless Integration
Unprecedented Efficiency
Reimagined Consumption
Diverse GPU Support
Seamless Integration
Built on top of our CUDA abstraction layer to support heterogeneous GPU vendors
Run PyTorch models inside GPU platform-agnostic containers
Maximize utilization with the dynamic allocation of GPU resources across workloads
Standardized runtime that eliminates environment reconfiguration and hardware compatibility issues
Currently, to run CUDA-GPU-accelerated workloads inside K8s pods, your K8s nodes must have an NVIDIA GPU exposed and the appropriate GPU libraries installed. In this
At WoolyAI, we’ve built a technology stack that decouples kernel execution from CUDA by introducing our own abstraction layer. Within this layer, kernels are compiled
