For ML Platform, MLOps, and Infra Teams

Run more AI workloads per GPU without changing your stack

WoolyAl is a runtime for dynamic sharing of GPU cores and VRAM on NVIDIA GPUs that integrates directly into your existing training and inference stacks.

Most notebooks, experiments, and inference workloads are bursty or only partially use the device. Static GPU allocation leaves expensive compute and VRAM stranded. WoolyAl helps you reclaim that capacity without changing application code.

Before WoolyAI

Notebook A

1 GPU reserved

Compute

35%

VRAM

48%

⚠️ idle capacity stranded

HPO Trial B

1 GPU reserved

Compute

22%

VRAM

40%

⚠️ idle capacity stranded

Inference C

1 GPU reserved

Compute

67%

VRAM

50%

⚠️ idle capacity stranded

After WoolyAI

Shared GPU Runtime

Higher Density

Notebook A

Active Share

HPO Trial B

Active Share

Inference C

Active Share

LoRA D

Active Share

Total Cores Utilization

100%

Managed VRAM Residency

89%

Weight Dedup

Dynamic Cores

VRAM overcommit

More jobs per GPU • Policy-aware granular sharing

Share GPUs smarter: Deterministic Cores 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

Benefits

Stop Giving Every Notebook a Whole GPU

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

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

WoolyAI protects priority workloads while background work fills idle capacity

Serve Many LoRA Variants Without Reloading the Base Model

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.

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