Announcing the launch of Private AI Labs Program – Up to $50K in infrastructure usage credits

Posted on April 14, 2025 by Sash Ghosh

Private AI Labs Program

Introducing the Private AI Labs Program: Your Gateway to Building AI on Private Infrastructure

The AI boom has arrived—and with it, an explosion of demand for secure, high-performance compute infrastructure. But while the possibilities of AI are vast, the challenges of building, testing, and scaling real-world AI workloads are very real. That’s why we’re excited to introduce the Private AI Labs Program—a new initiative from OpenMetal designed to help AI builders access enterprise-grade GPU infrastructure on a platform that puts privacy, performance, and flexibility first.

Why We Created the Private AI Labs Program

AI innovation shouldn’t be limited by infrastructure roadblocks. Whether you’re developing LLMs, experimenting with inference at scale, or building AI into your products, you need reliable access to high-powered GPUs—without the public cloud noise, shared tenancy limitations, or unpredictable costs.

The Private AI Labs Program is built to give startups, researchers, and enterprise teams the resources they need to accelerate AI projects—without compromising privacy or performance.

What You Get

Approved participants in the program can receive up to $50,000 in usage credits to run their AI workloads on OpenMetal’s GPU Servers and Clusters. Our infrastructure includes top-tier purpose-built NVIDIA A100 and H100 GPUs, built for demanding compute tasks like training and inference on large-scale models.

You’ll also get:

  • Private, dedicated GPU hardware – no noisy neighbors, no shared tenancy
  • High-bandwidth, low-latency networking – ideal for data-intensive workloads
  • Access to our team of infrastructure experts – to help you deploy, optimize, and scale
  • A chance to be featured as a real-world success story on our platform and marketing channels

Whether you’re a startup validating a new idea or an enterprise exploring the shift from public to private infrastructure, we’re here to support your AI journey.

Apply Today

Program Info and Application Form

Who Should Apply?

The Private AI Labs Program is ideal for:

  • AI/ML startups needing powerful, private infrastructure for development and testing
  • Researchers running compute-heavy training workloads
  • Enterprises evaluating infrastructure options for AI integration
  • Teams looking to transition from unpredictable public cloud costs to fixed, reliable infrastructure

If your use case is innovative, impactful, and GPU-intensive, we’d love to hear from you.

Start building on infrastructure that respects your need for privacy, supports your performance goals, and grows with your ambition.

Interested in GPU Servers and Clusters?

GPU Server Pricing

High-performance GPU hardware with detailed specs and transparent pricing.

View Options

Schedule a Consultation

Let’s discuss your GPU or AI needs and tailor a solution that fits your goals.

Schedule Meeting

Private AI Labs

$50k in credits to accelerate your AI project in a secure, private environment.

Apply Now

Explore More OpenMetal GPU and AI Content

With the new OpenMetal Private AI Labs program, you can access private GPU servers and clusters tailored for your AI projects. By joining, you’ll receive up to $50,000 in usage credits to test, build, and scale your AI workloads.

GPU Servers and Clusters are now available on OpenMetal—giving you dedicated access to enterprise-grade NVIDIA A100 and H100 GPUs on fully private, high-performance infrastructure.

Cold start latency becomes a visible and impactful factor in private environments and can slow down AI inference, especially when infrastructure is deployed on-demand to optimize resource usage or reduce costs. Learn causes, impacts, and how to reduce delay for faster, reliable performance.

Intel Advanced Matrix Extensions (AMX) is an instruction set designed to improve AI inference performance on CPUs. It enhances the execution of matrix multiplication operations—a core component of many deep learning workloads—directly on Intel Xeon processors. AMX is part of Intel’s broader move to make CPUs more viable for AI inference by introducing architectural accelerations that can significantly improve throughput without relying on GPUs.

Modern GPU technologies offer multiple methods for sharing hardware resources across workloads. Two widely used approaches are Multi-Instance GPU (MIG) and time-slicing. Both methods aim to improve utilization and reduce costs, but they differ significantly in implementation, performance, and isolation.

When comparing GPU costs between providers, the price of the GPU alone does not reflect the total cost or value of the service. The architecture of the deployment, access levels, support for GPU features, and billing models significantly affect long-term expenses and usability.