GPU Servers Built for AI/ML Workloads
Train on dedicated hardware, deploy at the edge
Access enterprise-grade GPU servers for training at a fraction of cloud costs, with seamless deployment to cloud edge locations for global inference.
AI Infrastructure Costs Are Unsustainable
Cloud GPU instances are prohibitively expensive for training. Reserved instances lock you in. Spot instances get interrupted. Meanwhile, your ML team is waiting for resources instead of iterating on models.
Key Features
Everything you need to optimize your infrastructure
Latest GPU Hardware
Access NVIDIA H100, A100, and L40S GPUs. NVLink interconnects for multi-GPU training. No waiting, no interruptions.
Dedicated Resources
No shared resources, no throttling, no spot instance interruptions. Your GPUs are yours 24/7.
Edge Inference
Train on bare metal, deploy to cloud edge locations worldwide. Sub-50ms inference latency globally.
Hybrid ML Pipeline
Seamless integration with AWS SageMaker, GCP Vertex AI, and Azure ML. Train anywhere, deploy everywhere.
High-Speed Storage
NVMe storage with 7GB/s read speeds. No data loading bottlenecks during training.
ML Ops Support
Our team includes ML infrastructure specialists who help optimize your training pipelines.
How It Works
Get started in four simple steps
Describe Your ML Workload
Tell us about your models, training data size, and inference requirements. We design a custom GPU cluster for your needs.
Provision Your GPU Cluster
We deploy dedicated GPU servers with your preferred ML frameworks pre-installed. Direct fiber links to cloud providers enable hybrid workflows.
Train at Full Speed
Run training jobs without interruptions or resource contention. Scale up or down as your needs evolve.
Deploy to Edge
Push trained models to cloud edge locations for low-latency inference worldwide, or run inference on dedicated hardware.
Real training costs for a computer vision startup
Training a large vision model: 8x A100 GPUs, 30 days continuous training
You save
53%
Monthly savings
$9,530/mo
* Comparisons based on public on-demand pricing. OverClouded pricing includes dedicated hardware and unmetered bandwidth.
"We cut our model training costs by 85% and reduced iteration time from days to hours. The dedicated H100 cluster pays for itself every month.
Frequently Asked Questions
Everything you need to know before getting started
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