Scale Your AI/ML Workloads with GPU Clusters

Accelerate your AI research and development with distributed GPU clusters across multiple cloud providers. Elemento provides seamless access to GPU resources, cost optimization, and framework integration for AI startups and research labs.

Multi-Cloud GPU Access

Access GPUs from AWS, Azure, GCP, and on-premise infrastructure through a single interface

Framework Integration

Native support for TensorFlow, PyTorch, Kubernetes, and other popular AI/ML frameworks

Cost Optimization

Intelligent resource allocation and auto-scaling to minimize costs while maximizing performance

Data Pipeline

Seamless data ingestion and preprocessing with GPU acceleration for large datasets

AI/ML infrastructure with GPU clusters and data processing

AI/ML-Ready Infrastructure

Complete platform designed for AI/ML workloads with GPU cluster management, framework integration, and cost optimization.

AtomOS hypervisor for AI/ML workloads

AtomOS

GPU-Ready Hypervisor

Production-grade hypervisor with GPU passthrough support, CUDA integration, and optimized performance for AI/ML workloads. Deploy on-premise or in hybrid cloud environments.

GPU passthrough CUDA integration Performance optimization
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Electros dashboard for AI/ML management

Electros

AI/ML Management Console

Unified dashboard for managing GPU clusters, monitoring training progress, and optimizing resource allocation. Real-time metrics, automated scaling, and cost tracking for AI/ML workloads.

GPU cluster management Training monitoring Cost optimization
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Atomosphere API for AI/ML integrations

Atomosphere

AI/ML Integration API

Comprehensive API for integrating with AI/ML frameworks, data sources, and cloud providers. Support for TensorFlow, PyTorch, Kubernetes, and automated model deployment.

Framework integration Data pipeline Model deployment
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