WoolyAI Acceleration Service

GPU service with GPU core and memory resources used billing

Website woolyai.com

What it is

Woolyai is now available as software that can be installed on-premise and on cloud GPU instances. With WoolyAI, you can run your ML PyTorch workloads in unified, portable (Nvidia and AMD) GPU containers, increasing GPU throughput from 40-50% to 80-90%.

Intent

I need it when

Maximize GPU utilization and reduce idle compute capacity in ML development environments

WoolyAI enables dynamic sharing of GPU cores and VRAM across multiple notebooks and experiments without code changes, allowing teams to run 3× more workloads per GPU by reclaiming idle capacity that static allocation leaves stranded.

Reduce queue wait times for ML experiments by overcommitting GPU memory intelligently

WoolyAI uses VRAM overcommit with smart swap eviction policies to place queued experiments immediately instead of waiting for exact VRAM fit, accelerating experiment throughput on the same hardware.

Integrate GPU optimization into existing ML platforms without application refactoring

WoolyAI operates as a drop-in Kubernetes Operator or Slurm integration with no code changes required, compatible with PyTorch, vLLM, Triton, and any CUDA application in current stacks.

Serve multiple inference endpoints cost-effectively without reserving full GPUs per model

WoolyAI enables multiple hot model endpoints to share GPUs with intelligent memory swapping, and deduplicates base model weights across LoRA variants, reducing per-endpoint GPU cost for inference providers.

Drop

Not a fit when

User requires static GPU allocation for guaranteed performance isolation between workloads
Organization uses non-NVIDIA GPUs or non-CUDA applications
User needs pricing transparency before trial signup to evaluate budget feasibility
Workloads require deterministic latency guarantees that dynamic sharing cannot provide
Infrastructure does not support Kubernetes Operator or Slurm integration

Commercials

Pricing

Pricing not specified