OpenMetal Launches Private GPU Servers and Clusters for High-Performance AI and HPC Workloads

OpenMetal Launches Private GPU Servers and Clusters for High-Performance AI and HPC Workloads

OpenMetal, a pioneer in on-demand private cloud infrastructure, announced the general availability of its new Private GPU Servers and GPU Clusters, delivering powerful, production-ready infrastructure for artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) workloads.

"Public cloud GPU access is riddled with limitations—premium pricing, throttled performance, and infrastructure you don't truly control," said Rafael Ramos, Director of Software Engineering at OpenMetal. "We built our GPU Servers and Clusters to provide a different experience: complete control, transparent pricing, and no compromises on performance or privacy."

Built for Builders and Innovators

Whether training large language models, deploying multi-node AI inference clusters, or running cutting-edge generative AI experiments, OpenMetal's infrastructure is designed to support the most demanding workloads. The platform can support popular AI frameworks such as PyTorch, TensorFlow, JAX, and Hugging Face Transformers.

Key Features of OpenMetal GPU Infrastructure:

  • NVIDIA GPUs: A100, H100 and more models for various performance and budget needs
  • Dedicated Bare Metal: No virtualization layer; customers have full control of hardware
  • Pricing Transparency: Monthly billing available with no hidden usage fees or out of control egress costs

Flexible Use Cases Across Industries

From startups training proprietary models to enterprises running inference at scale, OpenMetal's GPU offerings are built to accommodate a wide range of use cases. Healthcare, finance, and research sectors—where data locality, compliance, and infrastructure control are critical—are some industries that stand to benefit from OpenMetal's private, high-performance environment.

Customers can customize clusters to match their needs, including GPU counts, CPU/GPU pairings, RAM configurations, and storage volumes. The infrastructure is designed to be API-driven, enabling integration into existing DevOps pipelines and MLOps platforms.

Availability

GPU Servers and GPU Clusters are now available in both U.S. East and West regions, with expansion planned to additional zones in Europe and Asia later this year.