WoolyAI
GPU hypervisor for ML teams — run multiple AI experiments on a single GPU with no code changes.
| What is it | GPU hypervisor for ML teams — run multiple AI experiments on a single GPU with no code changes. |
|---|---|
| Pricing | Unknown |
| Platform | Web Application |
| API | Yes |
| Best for | Running multiple ML experiments concurrently on limited GPU resources, Reducing queue times for interactive notebooks and training jobs |
| Domain registered | 2024 |
Data updated Aug. 1, 2026
What does WoolyAI do?
WoolyAI is a GPU hypervisor designed for machine learning teams. It lets you run multiple CUDA workloads on a single NVIDIA GPU simultaneously, turning the standard model of one job per GPU on its head. This means you can pack more training jobs, inference tasks, or notebook sessions onto your existing hardware, effectively tripling your available compute capacity without buying new equipment.
The tool works through four core technical pillars. It handles GPU core-level scheduling to share processing power between jobs, allows for safe virtual RAM overcommit to fit more models into memory, deduplicates model weights in VRAM to save space, and decouples CPU and GPU resources for more flexible deployment. It integrates as a Kubernetes operator, so it works with your existing ML containers and platforms without requiring any changes to your code.
This is built for MLOps and ML platform teams who are managing clusters of expensive GPUs. It's particularly useful for running more hyperparameter optimization experiments concurrently, reducing idle time on interactive notebooks, serving multiple inference models on one GPU with guaranteed latency, and hosting many LoRA adapters on a single base model. If your team has a queue for GPU resources or is considering buying more hardware to meet demand, WoolyAI could help you do more with what you already have.
Key features
What makes it stand outWho is WoolyAI for?
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