WoolyAI Acceleration Service
A virtual GPU cloud providing scalable GPU memory and processing power
Cloud Service built on top of our CUDA Abstraction Layer
Your billing is based on actual GPU memory usage and core processing utilization
You execute your Pytorch projects in your CPU infrastructure
CUDA instructions are abstracted, transformed, regenerated and execute on WoolyAI GPU cloud service
docker exec wooly-container wooly credits
994239242
docker exec -it wooly-container python3 pytorch-project.py
torch.cuda.get_device_name(0): WoolyAI
model.safetensors: 100%|████████████████████| 3.09G/3.09G [00:14<00:00, 220MB/s]
generation_config.json: 100%|██████████████| 3.90k/3.90k [00:00<00:00, 37.1MB/s]
preprocessor_config.json: 100%|████████████████| 340/340 [00:00<00:00, 3.86MB/s]
tokenizer_config.json: 100%|█████████████████| 283k/283k [00:00<00:00, 4.93MB/s]
added_tokens.json: 100%|████████████████████| 34.6k/34.6k [00:00<00:00, 161MB/s]
Device set to use cuda:0
Coming Soon
Abstracted CUDA execution for cross-platform compatibility
Configures multi-vendor GPU infrastructure and presents it as a virtual GPU Cloud Service.
Can execute unmodified PyTorch CUDA workloads independent of the underlying GPU hardware.
Execute ML with your native container orchestration setup.
Execute concurrent workloads on the cluster and manage SLAs
