Dedicated infrastructure
GPU capacity reserved for your workloads alone. No shared resources, no data commingling.
Private GPU infrastructure for AI teams running model training, fine-tuning, inference and more.
Dedicated GPU infrastructure in US facilities — for teams whose training data requires isolation, security, and compliance.
Public cloud GPU is multi-tenant by design.
Weights, datasets and fine-tuning runs co-reside with other tenants.
Offshore capacity crosses borders your policy doesn't.
Transfer requirements and sovereign risk rule it out for regulated work.
Consumer cards die mid-run.
No ECC memory, no warranty, no tolerance for sustained load.
Reserved queues are measured in quarters.
Three to six months — past most project windows.
GPU capacity reserved for your workloads alone. No shared resources, no data commingling.
NVIDIA, AMD, or whatever the workload demands — optimized for compute, memory bandwidth, and interconnect at scale.
US and Europe-based facilities with no cross-border data transfer outside these regions and export-control compliance.
| Deep Leaf | Public cloud | Overseas | Consumer GPU | |
|---|---|---|---|---|
| Data stays isolated | Yes | No | Partial | No |
| US & Europe data residency | Yes | Depends | No | Depends |
| No shared infrastructure | Yes | No | No | No |
| ECC memory + full warranty | Yes | Yes | Varies | No |
| Available now | Yes | 3–6 mo | Varies | Yes |
Long-horizon runs on dedicated multi-node capacity — enterprise GPUs with ECC memory and 1 PB+ networked storage, so a forty-hour run finishes.
Iterate on proprietary datasets in an isolated environment. Adapters and checkpoints stay on hardware only your team touches.
Low-latency endpoints for production workloads. Keep inference on the same infrastructure you trained on — no model egress, no shared GPU access.
Live
Enterprise-grade infrastructure with redundant power, cooling, and connectivity.
Every deployment includes
24/7
Reliability and operations
Continuous DCGM telemetry watches every GPU, PSU and thermal envelope. On-site staff and remote hands are available at any hour, so a degrading part is replaced in a maintenance window — not discovered when a run dies at hour forty.
Chief Executive Officer
Co-founder and Former CEO of 4DS Memory, concept through commercial stage. Serial entrepreneur; VC Investor and Startup Advisor.
LinkedIn ↗
Chief Operating Officer
Managing Member of Giant Leaf LLC, infrastructure operations. J.D., University of Baltimore. Angel investor and advisor.
LinkedIn ↗
Chief Technology Officer
Technology executive with 25+ years delivering enterprise infrastructure and data center solutions for global organizations.
As VP, transformed an IT asset disposition firm into a global provider, acquired in 2018. Worked with hyperscale operators on secure data migration and compliance.
Deep expertise in data center infrastructure and technology strategy, guiding the company's technical vision and growth.
Chief Financial Officer
27-year financial veteran focused on accounting and finance. Deep focus in P&L management, business analysis, budgeting, compliance and investor relations, with a track record of successful exits and building high-performing, operations-aligned finance teams.
LinkedIn ↗Tell us the shape of your workload — GPU count, run duration, data-handling requirements — and we'll come back with a configuration, compliance documentation and availability.
Enterprise AI teams whose training data is sensitive or proprietary — healthcare, financial services and gov-adjacent workloads in particular, plus any team whose data policy rules out multi-tenant cloud.
Choose multi-tenant for cost efficiency or single-tenant for full isolation. Your workloads run on appropriately configured hardware with clear data boundaries.
In US and Europe-based data centres with domestic residency and no cross-border transfer outside these regions.
We host the right GPU for the job — NVIDIA, AMD, or whatever the workload demands — optimized for compute, memory bandwidth, and interconnect at scale.