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Edera for
GPUs

We are actively building – join us as a design partner!
Benefits
Resource Efficiency
Partition GPUs among multiple workloads, dramatically improving utilization from 20-30% to 60-80%.
Cost Optimization
Fewer physical GPUs needed when you can share them efficiently across teams and applications.
Multi-Tenancy
Safely run multiple teams, projects, or customers on shared GPU infra with guaranteed isolation.
Dynamic Allocation
Shift GPU resources between workloads based on changing priorities without changing hardware.
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 infra and 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-aaS platform for your entire organization.

See Edera in Action

The future of AI is secure, or it is limited. Ready to build the secure future? We are accepting design partners.
Diagram showing Edera Zones with isolated kernels managed by Edera on cloud or bare-metal infrastructure.

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

Become a design partner
The future of AI is secure...or it is limited
Let's build the future, together
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