Run More ML Experiments per GPU

GPU Hypervisor for ML Platforms

MLOps · ML Infra · Platform

GPU utilization up 3x

More jobs per GPU

No Code Changes

Do more experiments per GPU. Cut queue times. Delay your next GPU purchase.

Built for ML platform & MLOps teams running CUDA workloads on NVIDIA.

From 1 Job, 1 GPU

→ Many Jobs per GPU

Notebook/Pipeline

Your existing ML Pods/Containers + WoolyAI Runtime libraries

Your Shared GPU Pool (NVIDIA) with WoolyAI Server Hypervisor

Core Scheduling across Kernels

Safe VRAM Overcommit

Model Weight Dedup in VRAM

Share GPUs smarter: Deterministic SM scheduling, VRAM overcommit, Weight dedup

Pillar 1

Scheduling

(GPU core-level)

Fractional Core Allocation

Priority-Based Core Sharing

Elastic Core Redistribution

Pillar 2

VRAM

Virtualization

Elastic VRAM Overcommit

Max-Density Scheduling

Smart Swap Eviction

Pillar 3

Weights

Dedup

Shared Weights Dedup

Lower VRAM Footprint

Faster cold starts

Pillar 4

CPU-GPU

Decoupling

CPU Pods Accelerated

Transparent GPU Offload

Route-to-Any GPU

Higher Utilization

Faster Queue Times

Balanced Cluster

Placement Flexibility

Built for MLOps & ML Platform Teams

Interactive Notebooks Without Wasting GPUs

Reclaim idle gaps and keep the GPU busy while preserving responsiveness.

Pack More Experiments Per GPU for HPO, Sweeps & Ablations

Run many small trials concurrently instead of one GPU per run.

Latency-Protected Multi-Tenant Inference

Guarantee priority workloads while safely sharing the GPU with background workloads.

Serve Many LoRA Adapters on One Base Model

Deduplicate shared base weights so VRAM scales with adapters, not full models.

Co-Exists With Your Existing ML Platform

Integration Model

Drop-in compatibility with your existing ML platform!

Works with your existing ML containers

Deploy with WoolyAI's Kubernetes GPU Operator

See WoolyAI on Your Fleet

Step 1

Step 2

(5 min setup)

Measure headroom -> Review findings -> Plan rollout