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

Why use WoolyAI

Stop Giving Every Notebook a Whole GPU

Notebook sessions are bursty, but most schedulers still reserve an entire device.

WoolyAI places jobs beyond static VRAM-fit constraints and reclaims idle compute and VRAM between bursts, keeping interactive users responsive while overall GPU utilization rises.

Run Many Small Trials on the Same GPU

Hyperparameter sweeps and ablations often underutilize both VRAM and compute, yet schedulers still treat them as full-GPU jobs.

WoolyAI packs multiple partial-demand runs onto the same GPUs and dynamically allocates cores as trials speed up or stall.

Protect Premium Inference Without Wasting the Rest of the GPU

Most platforms isolate latency-sensitive inference by over-reserving GPUs, leaving capacity stranded.

WoolyAI protects priority classes while background work fills idle capacity

Serve Many LoRA Variants Without Reloading the Base Model

Today, multiple fine-tuned variants often duplicate the same base weights in VRAM.

WoolyAI deduplicates shared base-model weights so memory grows with adapter state and KV/runtime state, not repeated copies of the full model.

Capability Comparison

	Queueing / Preemption	Time-slicing / MIG	Budgets / Quotas	WoolyAl (4 pillars)
Concurrent Job Execution	✕	~	✕	✓ Dynamic core sharing
Enforce priority / SLA inside the GPU	~	~	~	✓ Priority-based core allocation
Place more jobs than physical VRAM	✕	✕	✕	✓ VRAM overcommit + swap policy
Deduplicate base model weights across apps	✕	✕	✕	✓ Shared weights in VRAM
Drop-in compatibility with existing pods/containers	✓	✓	✓	✓ No code changes
Kubernetes-native deployment model	✓	✓	✓	✓ Operator model

Other tools schedule between jobs. WoolyAl schedules within the GPU.

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

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 or Slurm

See WoolyAI on Your Fleet

Step 1

Step 2

(5 min setup)

Measure headroom -> Review findings -> Plan rollout