/// LOCAL AI INFRASTRUCTURE

Take Back Control of Your Compute

Companies lost control of their data and infrastructure to the cloud. Nerdit is the abstraction layer that turns your local GPUs into a sovereign, cloud-like platform. No DevOps required.

$pip install nerdit
60-80%vs Cloud Costs
5 minTo Deploy
100%On-Premise
nerdit - ~
/// DATA SOVEREIGNTY

0 bytes leave your infrastructure.
Ever.

100%

of your cloud data accessible to US authorities without notification.

CLOUD Act — DoJ.gov
3%

of global revenue at risk if training data traceability is not ensured.

EU AI Act — August 2025
44%

of enterprises suffered at least one cloud data breach in the past year.

Thales Cloud Security Study 2024

Nerdit runs 100% on your hardware. No data leaves your network. No external telemetry. GDPR and AI Act compliant by design.

/// THE CLOUD TRAP

Cloud was the only choice.
It no longer is.

1
The Promise

Cloud offered simplicity and scalability. Companies migrated by pragmatism — it was the rational move.

2
The Trap

Vendor lock-in. Costs spiraling with 30% waste. Training data stored on foreign servers, out of your control.

3
The Reckoning

€21B spent on cloud by the CAC40 alone. All of it subject to the CLOUD Act. No viable alternative — until now.

Cloud isn't a bad choice — it was the only one. Companies lack viable tooling to manage local infrastructure simply. That's what Nerdit changes.

/// MARKET OPPORTUNITY

A market in structural shift

83%/yr

Growth of European sovereign cloud IaaS market.

Gartner 2026
96%

of organizations report positive ROI on data privacy investment.

Cisco Data Privacy Benchmark 2025
61%

of European CISOs changing cloud strategy for geopolitical reasons.

Gartner 2025
/// FEATURES

Everything you'd expect from the cloud.
Nothing leaves your network.

Enterprise-grade orchestration with a developer-friendly CLI.

[01]

5-Minute Setup

One command installs. Auto-detects GPUs, CUDA, Docker. No system dependencies beyond Python and NVIDIA drivers.

$ nerdit init → scan → ready
[02]

Auto-Healing

Crash detection and automatic recovery. Failed jobs retry with GPU reallocation. Your training keeps running.

exit 137 → retry 1/3 → GPU reallocated
[03]

Smart Scheduling

Priority queue with first-fit allocation. Jobs land on idle GPUs automatically. No manual assignment.

priority=9 → first-fit on idle GPUs → run
[04]

Real-Time Monitoring

Live utilization, temperature, VRAM tracking. One command for full cluster visibility.

$ nerdit status → utilization, temp, VRAM
[05]

Container Isolation

Every job runs in its own CUDA container. Workspace mounts, reproducible environments, zero conflicts.

nvidia/cuda:12.4 → /workspace mount → run
[06]

Multi-Node Ready

Scale across machines with gRPC mesh networking. Add nodes to your cluster as you grow.

node1 ←gRPC→ node2 ←gRPC→ nodeN
/// HOW IT WORKS

From install to training
in four commands.

[01] INSTALL
$ pip install nerdit

Single binary. No system dependencies beyond Python, Docker, and NVIDIA drivers.

[02] INITIALIZE
$ nerdit init

Auto-detects GPUs, CUDA version, and Docker runtime. Cluster ready in seconds.

[03] RUN
$ nerdit run train.py --gpus 2

Submit jobs with GPU requirements. Smart scheduler handles allocation.

[04] MONITOR
$ nerdit status

Real-time utilization, job queue, and cluster health at a glance.

/// BETA ACCESS

Ready to own your
compute?

Join the beta and help shape the future of local AI infrastructure.

Early Access Priority Support Shape the Roadmap