Spheron Overview
Spheron is an aggregated GPU cloud platform that provides developers, ML teams, and AI startups with access to enterprise-grade compute resources at significantly reduced costs.
What is Spheron?
Spheron is not a blockchain network. It's a modern GPU cloud that aggregates capacity from multiple providers worldwide and exposes it through a unified dashboard. This approach eliminates vendor lock-in, scattered marketplaces, and high cloud bills while providing access to a global GPU fleet.
Key Features
Full VM Access
Get complete root access to enterprise GPUs. Install your own drivers, configure system settings, and manage your environment without container restrictions.
Bare Metal Performance
All instances run on true bare metal without hypervisor or virtualization layers. Your models receive full GPU power, resulting in faster training speeds and stable performance for long-running jobs.
Aggregated Network
Access 2,000+ GPUs across 150+ global regions from a single platform. This distributed architecture provides better availability, resilience, and pricing flexibility.
Hardware Variety
Choose from:
- High-end: SXM5 H100 machines with NVLink and InfiniBand for large-scale training
- Mid-tier: A100 GPUs for production workloads
- Budget-friendly: RTX 4090 and other PCIe GPUs for development and testing
Cost Savings
Spheron reduces GPU costs by 60-80% compared to traditional cloud providers:
- RTX 4090: ~$0.52/hr (37% cheaper than Lambda Labs, 45% cheaper than GPU Mart)
- Traditional clouds: Typically charge 3-4x more for equivalent GPU resources
- No hidden fees: Zero ingress/egress charges, transparent billing
Performance Benefits
- Faster Training: Bare metal architecture eliminates virtualization overhead
- Better Throughput: Optimized for distributed multi-node training
- Stable Performance: Consistent speeds for long training runs
- Low Latency: Built-in CDN for global data access
Platform Advantages
Reliability
Distributed across multiple regions and providers ensures your workloads aren't dependent on a single datacenter. Regional failures don't affect your ability to deploy.
Scalability
Start with a single GPU or scale to large clusters. Grow or shrink your compute footprint on demand without commitments.
Security
Choose secure datacenter providers for compliance-sensitive workloads. Trusted by AI startups as their primary GPU infrastructure.
Ease of Use
- One-click deployment
- API and SDKs for automation
- Real-time metrics and monitoring
- Auto-scaling groups
- Simple, transparent billing
How Spheron Compares
| Feature | Spheron | Traditional Clouds | Other GPU Clouds |
|---|---|---|---|
| Root Access | ✅ Full by default | ⚠️ Limited | ⚠️ Container-only (some) |
| Architecture | ✅ Bare metal | ❌ Virtualized | ⚠️ Mixed |
| Provider Model | ✅ Aggregated | ❌ Single vendor | ❌ Single vendor |
| High-end GPUs | ✅ SXM + NVLink | ⚠️ Limited | ⚠️ Limited |
| Pricing | ✅ 60-80% cheaper | ❌ Premium | ⚠️ Moderate |
Use Cases
Spheron is ideal for:
- LLM Training & Fine-tuning: Large language model development with multi-GPU support
- Research Workloads: Academic and corporate AI research projects
- Production Inference: Deploy trained models for real-time predictions
- Distributed Training: Multi-node training with high-speed interconnects
- Development & Testing: Cost-effective GPUs for prototyping
Next Steps
- Getting Started - Deploy your first instance in 5 minutes
- Quick Start - Launch pre-configured models
- Reserved GPUs - Lock in long-term GPU access for better rates
- Billing - Understand pricing and payment options