Ideal for MLOps, ML Platform, and Infra teams operating multi-tenant GPU clusters.
GPU Hypervisor for ML Platforms
MLOps · ML Infra · Platform
Do more experiments per GPU. Cut queue times. Delay your next GPU purchase.
Built for ML platform & MLOps teams running CUDA workloads on NVIDIA.
Notebook/Pipeline
Your ML Pods/Containers with WoolyAI Runtime libraries
Kernel-level Scheduling
Safe VRAM Overcommit
What WoolyAI Does
Pack more notebooks, experiments, and training jobs onto each GPU—without noisy neighbors.
Small jobs start immediately, rather than waiting behind long-running workloads or reserving entire GPUs.
Balance load across the cluster and eliminate stranded GPU capacity.
GPU scheduling is no longer tied to container placement enabling higher cluster efficiency, and shorter wait times.
WoolyAI schedules GPU compute at the kernel level, allocating GPU cores with deterministic, SLA-based guarantees instead of GPU-slicing or coarse job-level time-slicing.
GPU memory is treated as a managed, virtual resource—allowing safe VRAM overcommit so multiple workloads can share memory efficiently.
Identical model weights are loaded once and shared across applications, eliminating redundant VRAM usage and enabling more workloads per GPU.
Option to run ML containers on CPU, while all GPU operations are transparently executed on a shared GPU pool—no code changes required.
Let 20–50 researchers share a small GPU pool without noisy neighbors or queueing.
Run 2-5x more experiments per GPU without new hardware
Guarantee latency for critical endpoints while filling idle SMs with background jobs.
Run many fine-tuning jobs simultaneously without scaling the GPU count.
Curious how much headroom you actually have in your current GPU cluster? Try out our open Source utilization monitor tool and book a demo of WoolyAI.
Ideal for MLOps, ML Platform, and Infra teams operating multi-tenant GPU clusters.