Nvidia Hardware
AMD Hardware
In today’s setups, CUDA containers must run on the GPU hosts, which locks developers to scarce machines, exposes keys and data on shared accelerators, complicates patching/drivers, and makes multi-tenant control messy. With Wooly abstraction, you build and run unchanged PyTorch code inside a CPU-only Wooly Client container. At the same time, the CUDA kernels are sent as Wooly Instruction Set (WIS) to GPU servers running the Wooly Hypervisor, which JIT-compiles to native CUDA/ROCm.
Unlike traditional GPU infrastructure management approaches, which rely on static time-slicing or static partitioning, WoolyAI JIT compiler and runtime stack can measure and dynamically reallocate GPU compute cores across concurrent ML workload processes based on real-time usage, workload priority, and VRAM availability. This results in 100% utilization of GPU compute cores consistently.
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 cores 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.
Serve many ML teams (training, inference, fine-tuning, RL, vision, recommender systems) on single or multi GPU node cluster without hard partitioning and with job isolation, predictable SLA, unified pipeline for both Nvidia and AMD.
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 100% utilization.
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.
Develop and manage ML workload containers on CPU only infrastructure, while the CUDA kernels execute on remote shared GPU pool. The result is flexible, secure remote execution with cleaner ops and policy control
Run many concurrent CI/CD pipelines on single GPUs in an isolated compute/memory sandbox, scheduler dynamically adjusts GPU cores and memory allocation to meet SLA and job priority, run pipelines on both Nvidia and AMD without duplication.
Wooly AI was created by a world-class virtualization team with decades of experience developing and selling virtualization solutions to enterprise customers.
