Hypervise Maximize Optimize YOUR ML INFRA

With Wooly AI, the world’s first hardware-agnostic hypervisor for ML GPU clusters.

The Wooly AI Difference

Cross-vendor CUDA Execution

With Wooly Hypervisor Just-In-Time (JIT) compilation, we enable the execution of unmodified PyTorch and other CUDA applications in heterogeneous GPU vendor environments that are on-premises, in the cloud, or both.

PyTorch/vLLM Application

Wooly GPU Hypervisor

Just-In-Time Compilation

Nvidia Hardware

AMD Hardware

Maximum Utilization & Concurrency

By precisely controlling how kernels execute on the GPU, we enable real-time, usage-aware scheduling that adapts to actual resource demand, not fixed time slices.

Key Benefits of Wooly AI

GPU independence

On both Nvidia and AMD

Higher ML Ops team productivity

No code rewrites and less time spent right-sizing & optimizing GPU clusters

Faster deployment of ML apps

Concurrent isolated execution of models to test, roll out, and iterate on AI features without infrastructure bottlenecks

Maximized infrastructure utilization

Dynamic GPU core and memory sharing at runtime across different ML applications based on demand and priority

Peace of mind

Multi-model orchestration across clusters with native performance and reliability

Greater operational flexibility

Live migration of ML apps across GPU clusters. Works with on-prem GPUs, cloud GPU instances, and hybrid environments.

Wooly AI Use Cases

Consolidate multiple ML teams on shared infrastructure for both Nvidia and/or AMD GPUs

Serve many ML teams (training, inference, fine-tuning, RL, vision, recommender systems) on single or multi GPU cluster without hard partitioning and with job isolation, predictable SLA, unified pipeline for both Nvidia and AMD.

Improve GPU and VRAM utilization of Nvidia and/or AMD GPU cluster

Eliminate the “one-job-per-GPU” bottleneck with concurrent execution of fine-tuning, inference, training, or simulation jobs on single GPU, dynamic allocation and management of GPU compute cores and VRAM, Live-migrate or backfill jobs to underutilized GPUs, go from 40-50% GPU utilization to 80-90% utilization.

Cost effectively scale-out Nvidia GPU infrastructure with AMD GPU

Expand Nvidia only cluster with cost efficient AMD GPUs without any changes to existing CUDA ML workloads. Single unified Pytorch containers for both Nvidia and AMD with hardware aware optimization, centralized dynamic scheduling across mixed GPU clusters.

Migrate from Nvidia to AMD GPUs without rewriting any code

Run the same Pytorch CUDA code without any changes on AMD GPUs, Unified container to run Pytorch and inference on both Nvidia and AMD, Native performance on both CUDA and AMD with runtime leveraging hardware-aware optimizations for both hardware.

Set up CI/CD and Model A/B Testing Pipelines on shared infrastructure

Run many concurrent CI/CD pipelines on single GPUs in isolated compute/memory sandbox, scheduler dynamically adjusts GPU core and memory allocation to meet SLA and job priority, run pipelines on both Nvidia and AMD without duplication.

About Us

Wooly AI was  created  by a world-class virtualization team with decades of experience developing and selling virtualization solutions to enterprise customers. 

Wooly AI is currently in private beta.