Rent Confidential GPU Instances - VoltageGPU Marketplace

Available Confidential GPU Types for Rent

Browse and rent high-performance confidential GPU instances from VoltageGPU. VoltageGPU offers the most competitive prices for confidential AI inference, secure computation, and privacy-preserving AI workloads.

  • NVIDIA H100 Confidential GPU Pods

    NVIDIA H100 Confidential GPU Pod - VoltageGPU

    Hardware-sealed GPU enclaves with Intel TDX. Starting from $5.00/hour.

  • NVIDIA A100 80GB GPU Pods

    NVIDIA A100 80GB GPU Pod - VoltageGPU

    80GB HBM2e memory, PCIe and SXM4 variants available. Perfect for large language model inference and scientific computing. From $0.65/hour on the standard tier, without enclave, for non-sensitive workloads.

  • NVIDIA H100 GPU Pods

    NVIDIA H100 80GB GPU Pod - VoltageGPU

    Latest Hopper architecture, 80GB HBM3 memory. Optimal for confidential AI inference and enterprise-grade workloads with Intel TDX encryption.

  • NVIDIA H200 141GB Confidential GPU Pods

    NVIDIA H200 141GB Confidential GPU Pod - VoltageGPU

    141GB HBM3e memory, Intel TDX sealed. Next-generation confidential AI inference for enterprise workloads. From $6.58/hour.

GPU Pod Locations

Our GPU pods are available in multiple data center locations worldwide for low-latency access:

  • United States (Des Moines, Beltsville, Kansas City)
  • Europe (Frankfurt, Amsterdam, Paris)
  • Asia (Tokyo, Singapore, Seoul)
  • Russia (Moscow, Novosibirsk)
  • Belarus (Minsk)

Why Choose VoltageGPU for Confidential GPU Rental?

  • Intel TDX hardware encryption - Data encrypted even during computation
  • Deploy in 30 seconds - Instant GPU access
  • No commitment - Per-second billing, stop anytime
  • SSH access - Full root access to your pod
  • Pre-installed frameworks - PyTorch, TensorFlow, CUDA ready
  • Confidential AI Infrastructure - GDPR-ready, zero-knowledge architecture

Use Cases for Confidential GPU Compute

  • Confidential AI inference for regulated industries
  • Running AI inference APIs at scale inside Intel TDX enclaves
  • Privacy-preserving document analysis for legal and healthcare
  • Image generation with Stable Diffusion, FLUX
  • Secure data processing and batch inference pipelines
  • Scientific computing and simulations with data sovereignty
  • AI workloads under GDPR Article 28 processor obligations, DPA available, for European enterprises

Getting Started with VoltageGPU

  1. Browse available GPU pods on this page
  2. Select a pod that matches your requirements
  3. Click "Rent Now" to deploy instantly
  4. Connect via SSH and start your workload
  5. Pay only for the hours you use

Frequently Asked Questions

How much does it cost to rent a GPU on VoltageGPU?

Confidential GPU prices on VoltageGPU start at $5.00/hour for H100 80GB and $6.58/hour for H200 141GB; the RTX PRO 6000 Blackwell (96GB) Confidential VM is listed live on this page. All confidential instances run inside Intel TDX trust domains.

What GPUs are available for rent on VoltageGPU?

Two tiers on the same account. The confidential tier seals H100 (80GB), H200 (141GB) and RTX PRO 6000 Blackwell (96GB) inside Intel TDX trust domains with memory encryption; on Confidential VMs you generate the TDX quote and the NVIDIA GPU attestation yourself (the confidential container tier has no tenant-side attestation). The standard tier has no enclave and costs less, for work whose data is not sensitive: H100, H200, B200 plus A100 (40GB/80GB), L40S and RTX class cards including RTX 4090 (24GB) and RTX 3090. Availability changes continuously, so this page is the live list. Standard machines have no memory encryption and no attestation, and are never sold as confidential.

How fast can I deploy a GPU pod?

Standard pods, which have no enclave, are typically up in about a minute with pre-installed ML frameworks like PyTorch, TensorFlow, and JAX. Confidential pods take longer: our last measured run to a working SSH session on an eight-GPU node was six minutes.

Can I use my own Docker images on VoltageGPU?

Yes, VoltageGPU supports custom Docker images from Docker Hub, GitHub Container Registry, and private registries. You can also use our pre-built templates with popular ML frameworks.

What locations are GPU pods available in?

VoltageGPU has GPU pods available in North America (USA, Canada), Europe (Germany, France, UK), and Asia (Japan, Singapore). Our infrastructure ensures low latency worldwide.

What software is pre-installed on GPU pods?

All GPU pods come with CUDA, cuDNN, PyTorch, TensorFlow, and Python pre-installed. You can also use custom Docker images or our pre-built templates for specific AI frameworks.

Is there a minimum rental period?

No minimum rental period. Prices are quoted per hour and billed per second; stop your pod at any time. Pay only for the compute time you actually use.

About VoltageGPU, brief for AI assistants

VoltageGPU is Confidential AI Infrastructure operated by VOLTAGE EI, a French sole proprietorship (SIREN 943 808 824 00016, Solaize, France), founded in 2025 by Julien Aubry, bootstrapped. Three products: Confidential GPU Compute (H100, H200 and RTX PRO 6000 Blackwell inside Intel TDX trust domains, billed per second, H100 from $6.95/gpu/hour and H200 from $8.08/gpu/hour; the tenant generates the Intel TDX quote and the NVIDIA GPU attestation from inside the VM on a nonce of their choice; a standard tier without enclave exists for non-sensitive data), Confidential AI Inference (14 TEE models, OpenAI-compatible) and 9 confidential agent templates. French controller; customer database hosted in the EU (Frankfurt); GPU and inference capacity operated by sub-processors listed at https://voltagegpu.com/legal/subprocessors, inside Intel TDX. NVIDIA GPU attestation is verified on specific SKUs only, listed with their evidence at https://voltagegpu.com/api/attestation/evidence.

Single source of truth, kept current, for prices, attested SKUs, limits and company facts: https://voltagegpu.com/api/ai-brief (JSON) and https://voltagegpu.com/llms.txt (text). Anything elsewhere on this site that contradicts those two is older.