Private Inference API
Training

Foundational model training

Train foundation models at scale on sovereign, bare-metal GPU infrastructure, enabling full hardware control, high performance, and efficient iteration on large datasets.

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Why bare-metal matters

01

Unfiltered access
to hardware

No virtualization overhead or noisy neighbors slowing you down.

02

Maximized
performance

GPUs operating at full capacity for distributed training and optimized compute. 

03

Predictable
scaling

Fom hundreds to 100K+ GPUs, interconnected with ultra-high throughput networking.

04

Sovereign
control

Keep compute and data fully inside your regulatory domain.  

What the AI
Factory provides

Industrial-scale
GPU clusters

A private GPU cloud built for foundation model training, with thousands of NVIDIA GPUs connected through high-speed interconnects and scalable from small prototypes to 100K+ GPU deployments.

Direct hardware control

Workloads run directly on the hardware, without virtualization overhead, enabling full configuration control and consistent, predictable performance for large-scale training.

Sovereign infrastructure

Data, compute, and orchestration stay fully within your control, operating in compliant regions with strict boundaries around data residency, security, and intellectual property at all times.

The AI Factory advantage

With bare-metal access to sovereign GPU infrastructure and tooling designed for industrial-scale training, the AI Factory is where foundation models are born and hardened. From research to production-grade models, it provides the performance, control, and governance modern AI builders demand.

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From concept
to trained model

01

Plan & architect

Together with specialists, the required GPU setup is defined based on model size, dataset characteristics, and the chosen parallelism strategy. This results in a tailored cluster design that balances performance, scalability, and cost efficiency.

02

Provision bare-metal clusters

Dedicated GPU hardware is deployed with the latest accelerators, high-bandwidth interconnects, and essential training frameworks. With direct bare-metal access, teams can start training immediately, without virtualization layers affecting setup or performance.

03

Run & optimize training

Training runs with full hardware control, using preferred schedulers such as Slurm, Kubernetes, or custom solutions. GPU utilization, network throughput, and training progress are continuously monitored and optimized throughout execution.

04

Evaluate, iterate & deploy

Trained models are evaluated against benchmarks or custom metrics, refined through fine-tuning, and prepared for production. Once validated, models are deployed to inference clusters or integrated into downstream pipelines.

Performance &
cost efficiency

Unlike public cloud GPU instances that abstract hardware behind layers of software, bare-metal access:
  • Reduces latency and variability
  • Increases usable GPU throughput
  • Improves cost predictability
16
x
faster*
50
%
cheaper*

*compared to Azure

Train smarter. Train larger. Train where you own the compute.

Talk to an expert
Dedicated Inference
Private GPU infrastructure for enterprise AI
Dedicated Inference gives organizations full control over AI inference at scale, with dedicated GPUs, predictable performance, and complete control over data and cost.
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Fine-tuning
Expert model tuning and deployment
For scenarios where RAG alone can’t deliver. Finetuning embeds your knowledge, style, and processes directly into the model, with full privacy and enterprise-grade control.
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