AI Workspace
GPU-ready AI environments, delivered and managed
Give data science and AI teams a GPU-backed workspace with the tools already in place — so they train, experiment, and ship instead of provisioning infrastructure.
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What teams are dealing with
AI and data science work needs serious compute and a carefully built software stack — GPUs, drivers, frameworks, and notebooks all lined up correctly. Sourcing GPU capacity, configuring the stack, and keeping it consistent across a team is heavy infrastructure work that delays the actual research and modelling.
Everything, delivered and managed
- 01 GPU-backed workspaces for training, experimentation, and heavy compute — live today.
- 02 A ready-to-work stack — notebooks, frameworks, and drivers (e.g. Jupyter, CUDA, common ML libraries) preconfigured.
- 03 Fast shared and object storage next to the compute, inside your security boundary.
- 04 Identical, reproducible environments for every researcher.
- 05 Managed end-to-end — provisioning, updates, and security handled for you.
What changes for your teams
Data science teams start modelling on day one, not after infrastructure setup.
GPU capacity is delivered as a ready workspace, not a build project.
Consistent environments make results reproducible across the team.
Data stays close to the compute and inside your security boundary.
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Start with the right solution for your teams
Tell us about your teams and systems, and we'll help you map the right DigiEdge Workspace solution — and how it would deploy in your environment.