AltaAI DCO abstracts geographically dispersed, fragmented edge nodes and mismatched server racks into a single, high-performance virtual computing resource.
Aggregate mismatching hardware (differing VRAM configurations, processing speeds, GPU architectures) into a single virtual pool. Automatically scale and distribute workloads across locations (from 100kW edge nodes to 5MW data centers) without overloading local electrical grids.
Instantly provision dedicated, bare-metal virtual compute clusters across multiple physical locations for priority deep learning model training. Complete multi-tenant isolation ensures data security on shared infrastructure.
Migrate active deep learning training jobs from one physical location to another with zero state loss or connection drops. Workloads are relocated automatically in response to thermal thresholds or utility power fluctuations.
Maximize private optical fiber performance. Monitor link integrity and physical fiber paths in real-time to cluster distributed memory pools and reroute active traffic around network failures.
Eliminate idle GPUs at small edge nodes. Cluster them together to monetize previously underutilized computing power.
Scale compute tasks smoothly beyond the limitations of any single server room or municipal electrical grid.
If a localized facility faces electrical drops or thermal limits, workloads relocate to healthy nodes on the fiber network instantly.
Developers access a single unified API endpoint, avoiding infrastructure configuration or physical routing complexities.
No. Unlike legacy systems that require symmetrical hardware clusters, DCO virtualizes mismatched configurations. It supports mixing GPU architectures, varying VRAM profiles, and differing system RAM counts into a unified pool without causing pipeline blockages.
DCO establishes low-latency, point-to-point network loops. When a relocation event triggers (such as a local power grid fluctuation), DCO transfers execution states and memory footprints across the fiber path, resuming execution at the target node without requiring a job restart.
For tightly-coupled parallel training, node clustering requires dedicated point-to-point fiber rings with latency limits under 5ms. For loosely-coupled batch inference, DCO supports nodes connected over standard WAN pathways with latency profiles up to 45ms.
