Unified GPU Pool
Pool scattered GPU servers into one cluster that belongs to the organization, not to one team.
NADOO CLOUD ON-PREMISE
Turn Your Existing GPUs Into a Shared Cloud
Pool your on-premise GPU servers into a shared service that developers can request and release on demand—without buying new hardware.
Template
PyTorch 2.4 + CUDA 12.4
AI training
1–8 GPU · JupyterLab
vLLM Inference Server
LLM inference
1–8 GPU · OpenAI Compatible
Stable Diffusion WebUI
Image generation
1–2 GPU · WebUI
Resources
GPU
NVIDIA A100 80GB
GPU count
1 GPU
vCPU
8 vCPU
Memory
32 GB
Runtime
VM instance
Recommended
Runs as an isolated VM with the GPU and
resources you selected.
Isolated environment
Dedicated GPU and resources
Full root access
Summary
VM instance
GPU
NVIDIA A100 80GB
vCPU
8 vCPU
Memory
32 GB
+ Create instance
PROBLEMMore GPUs.
More GPUs.
The Same Manual Operations.
Adding hardware doesn't raise utilization.
The bottleneck isn't capacity — it's how you share it.
- 01
Infra and MLOps Talent Shortage
As AI factories multiply, engineers who understand GPUs, servers, model serving, and secure networks all at once become the bottleneck.
- 02
Monopolized Clusters, Idle GPUs
When shared hardware is locked to one team or left idle, you can't justify buying more.
- 03
No Showback, No Monitoring
No record of which team used how much of the centrally funded GPUs.
CAPABILITIES
From a Server under a Desk
to GPU-as-a-Service
Quotas and Reclaim
Set a quota per team once. Allocation stays inside it, and GPUs never released get flagged.
Request and Release
Developers provision what they need and release it when they're done. No operator in the loop.
Access and Audit
Control terminal, Jupyter, and IDE access, and log who did what and when.
HIGHLIGHTS
Admins Set the Quota.
Developers Do the Rest.
Handing out GPUs drops off the ops queue.
Admins watch quotas and utilization. Developers provision their own from the console.
Admins
One-command registration
Paste one command on a new GPU server and it joins the cluster.
Auto fault isolation
A faulty GPU drops out of scheduling, so no job lands on broken hardware.
Stale allocations
GPUs still held after a job ends get flagged in the list.
Per-team quotas
Set it once. Allocation stays inside it, and usage stays on record.
Developers
Ready in minutes
Pick and click. Frameworks and drivers are preinstalled.
Memory-level slicing
Several users share one card, so there's less waiting on the big machines.
No tickets
Stopping and restarting takes no request to an admin.
Persistent storage
Delete the instance and your datasets and checkpoints stay put. Nothing to re-upload.
One-click access
A web terminal and JupyterLab open right there. No access request needed.
Bring your own IDE
Attach the VS Code you already use.
ENTERPRISE
Your GPUs and Your Data Never Leave Your Server Room
Everything above works the same on an air-gapped network.
Data and workloads never leave your data center.
- On-premise deployment
- ISO 27001
- Air-gapped support
- No data egress
- Air-gapped upgrade and rollback
- Full audit logging
Before You Buy More GPUs,
Share the Ones You Have
We'll show you where to start and what a rollout takes.