Launch of Private GPU Servers & Clusters

We’re thrilled to announce the launch of Private GPU servers and GPU clusters on OpenMetal, purpose-built to support not only AI and machine learning workloads—but also a wide range of high-performance computing needs.

Whether you’re building models, running simulations, crunching big data, or working on video rendering, our enterprise-grade NVIDIA A100 and H100 GPUs are now available on fully dedicated, private infrastructure that delivers performance without compromise.

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Why Choose OpenMetal for Your GPU Infrastructure?

Private, Secure & Fully Yours

Unlike shared GPU offerings in the public cloud, OpenMetal gives you dedicated bare metal access to GPUs—meaning your resources are never throttled, shared, or exposed to noisy neighbors. You get full control over performance, data security, and compliance—critical for businesses handling sensitive or proprietary data.

Tailored to Your Workloads

Our infrastructure is fully customizable—from single GPU servers to large-scale clusters—so we can build them to meet your specific needs. GPU-powered use cases go far beyond AI/ML. The servers in our catalog can support:

  • Computational research
  • Financial modeling
  • Media rendering
  • Simulation & scientific workloads
  • Private inference endpoints
  • And much more.

Supported by Experts from Day One

At OpenMetal, you’re never on your own. Our team is with you from day one—consulting with you on the right architecture, supporting your deployment, and helping you optimize for success. We’re not just a provider—we’re your partner in performance.

Interested in GPU Servers and Clusters?

GPU Server Pricing

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

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Schedule a Consultation

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

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AI Deployment Guides

See which open-weight models fit each GPU and CPU server, and how to run or fine-tune them.

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Explore More OpenMetal GPU and AI Content

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InfiniBand vs Spectrum-X Ethernet for AI clusters: how NVIDIA’s Quantum-X800 InfiniBand and Spectrum-X Ethernet differ, what ‘InfiniBand-class’ means, and when each fits.

The answer is set by three things: the model’s weight footprint, the KV cache your context and concurrency produce, and how your serving framework spreads both across GPUs. An eight-GPU

The NVIDIA HGX B300 is NVIDIA’s Blackwell Ultra platform in its densest single-node form: eight B300 SXM GPUs wired together on one baseboard by fifth-generation NVLink and NVSwitch into a

A practical guide to matching NVIDIA’s H100, H200, B200, and B300 to your model, your latency target, and your budget, starting from the workload instead of the spec sheet. Key

What LangGraph manages and what it explicitly does not, why MemorySaver is not a production checkpointer, what PostgresSaver requires from your infrastructure, the compute profile of long-running agent workflows, and where dedicated bare metal fits.