Ideal for MLOps, ML Platform, and Infra teams operating multi-tenant GPU clusters.
GPU Hypervisor for ML Platforms
MLOps · ML Infra · Platform
WoolyAI lets notebooks, dev/test jobs, and small training runs execute on CPU-only nodes while their GPU kernels run on a shared remote GPU pool. You keep your existing MLOps stack — Kubernetes, Ray, Airflow, Slurm — and Wooly packs many workloads onto each GPU.
Do more experiments per GPU. Cut queue times. Delay your next GPU purchase.
Built for ML platform & MLOps teams running PyTorch workloads on NVIDIA.
Notebook/Pipeline
CPU Pod/VM running WoolyAI Client ML Container
Kernel-level Scheduling
Deterministic SLAs
Instead of slicing GPUs or time-slicing access, WoolyAI schedules GPU kernel execution from many jobs inside a shared GPU context. The result: multiple ML workloads per GPU, deterministic SLAs, and significantly higher utilization.
What WoolyAI Does
Your notebooks, pipelines, and batch jobs run on CPU-only infrastructure (Kubernetes nodes, VMs, or bare metal). From the user’s perspective, nothing changes — they still say device="cuda".
WoolyAI routes GPU operations from ML jobs into your shared pool of GPUs (NVIDIA and AMD), which can be on-prem, cloud/hosted, where a GPU hypervisor schedules and executes kernels.
WoolyAI captures PyTorch kernel launch events inside the ML Container, translates them into a GPU-agnostic Intermediate Representation, and on the GPU server, WoolyAI hypervisor JIT-compiles them to the target GPU ISA for execution with zero performance impact.
WoolyAI interleaves kernels from multiple jobs within the same GPU context, with priority-aware, SLA-driven scheduling.
Inside the ML container running ML code, WoolyAI intercepts and transparently forwards all GPU operations to your shared remote GPU pool. The application running on CPU only still thinks it’s using cuda:0.
GPUs stay busy, SMs stay hot, and high-priority workloads get predictable latency guarantees. Effective ML throughput per GPU increases by 3× in dev/experimentation fleets.
WoolyAI is designed to plug into your existing ML platform and immediately pay off in the parts of the lifecycle that hurt the most: notebooks, experiments, HPO, and pre-production training.
Let 20–50 researchers share a small GPU pool without noisy neighbors or queueing.
Run dozens of trials concurrently on the same GPUs. Share common base model across multiple trials with VRAM DeDuplication and optimize on VRAM usage.
Share GPUs across vision, embedding, and LLM stages.
Guarantee latency for critical endpoints while filling idle SMs with background jobs.
Run canary jobs as high priority without dedicating full GPUs.
Scale your Infra by adding AMD GPUs to your cluster without asking teams to change code.
WoolyAI works with Kubernetes, Ray, Airflow/Flyte/Metaflow, and any containerized ML workflow. Your teams keep their tools — you just get far more out of your GPUs.
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.