For ML Platform, MLOps, and Infra Teams

Run more AI experiments 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: Runtime Core 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

ML Teams - Run more notebooks on the same GPUs without slowing users down

Don’t reserve full GPUs the whole time for bursty Notebooks. WoolyAI reclaims idle compute and memory between bursts, keeping users responsive as GPU utilization rises.

ML teams - Start queued experiments sooner

Don’t let the experiments wait in the queue because the scheduler’s behavior requires an exact VRAM fit before placing them. WoolyAI overcommits GPU memory to start more experiments immediately, then uses smart swapping to keep multiple jobs running safely on the same GPU.

NeoClouds - Keep dedicated inference endpoints responsive without leaving GPUs half idle

Don’t reserve dedicated GPUs for bursty or low-demand model endpoints. WoolyAI will enable multiple hot model endpoints to share the same GPUs, swapping memory intelligently when needed, so providers can keep models ready without incurring full-GPU cost for each one.

NeoClouds - Serve more customer-specific LoRA endpoints on the same GPUs

Don’t waste precious GPU VRAM loading the same base model repeatedly for LoRA variant inference endpoints. WoolyAI keeps a single shared copy of the base weights in memory, so GPU usage scales mainly with adapter state and live runtime state rather than repeated full-model copies.

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