WoolyAI Acceleration Service
A GPU cloud with "Actual GPU Resources Used" billing, NOT "Time Used" billing
Cloud Service built on top of our WoolyStack 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
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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
Configure multi vendor GPU infrastructure and present it as a unified GPU cluster.
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
Deploy on Your Own Infrastructure –
Cloud, On-Prem or Air-Gapped.
