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Edera for
GPUs
AI-ready infrastructure that evolves with your business.



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Scaling GPU accessibility through true isolation. Edera’s platform safely slices GPU resources across workloads, enabling multiple applications to securely share infrastructure. Eliminate waste, reduce costs, maximize efficiency, and protect sensitive workloads.
Benefits
Resource Efficiency
Partition GPUs among multiple workloads, dramatically improving utilization from 20-30% to 60-80%.
Multi-Tenancy
Safely run multiple teams, projects, or customers on shared GPU infrastructure with guaranteed isolation.
Cost Optimization
Fewer physical GPUs needed when you can share them efficiently across teams and applications.
Dynamic Allocation
Shift GPU resources between workloads based on changing priorities without physical hardware changes.
Features
Multi-Framework Compatibility
Provide out-of-the-box support for all major AI/ML frameworks without requiring any changes to your code.
GPU Sharing & Virtualization
Allow multiple workloads to run securely on a single GPU by dividing its memory and compute resources into right-sized vGPUs.
Intelligent Scheduling
A sophisticated scheduler automates and optimizes GPU resource allocation.
Environment Agnostic
Seamlessly orchestrate GPU workloads on any infrastructure and direct support for bare metal environments.
Use Cases
Dense Inferencing
Virtualization co-locates multiple inference models on single GPUs, boosting utilization and cutting costs.
Model Development
Access to fractional GPUs for interactive research and experimentation; dramatically lowering the resource cost per user.
Distributed Training
Automatically pool and schedule GPU resources to run large training jobs across multi-node clusters.
Secure Multi-Tenancy
Build a centralized, shared GPU-as-a-service platform for your entire organization.
See Edera in Action
Our technology creates a standardized layer to manage GPU workloads at scale, abstracting away the painful complexities that plague engineers today.

Want to dig into the details? Head over to our docs site for more info!
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