ZibraLabs

Founded 2024

Run distributed machine learning compute workloads across multi-cloud CPU and GPU clusters

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You can use ZibraLabs to run massively parallel artificial intelligence workloads across distributed compute clusters. It aggregates low-cost CPUs, GPUs, and spot instances across multiple cloud providers and hyperscalers. The system handles large-scale parameter sweeps, multi-modal data pipelines, batch model inference, and reinforcement learning rollouts. With scheduling overhead below fifty milliseconds, it coordinates millions of tasks across thousands of hardware nodes. This allows engineering teams to execute heavy agent workflows and simulations without building custom distributed orchestration infrastructure from scratch.

What ZibraLabs does

Schedule distributed tasks across heterogeneous GPU clustersRun large parameter sweeps across thousands of compute nodesExecute high-volume batch model inference across multiple regionsDeploy long-horizon agent pipelines with complex multi-tool callsCoordinate reinforcement learning reward and rollout calculationsManage spot instance availability across different cloud vendorsAggregates CPUs and GPUs across hyperscalers and neocloudsScales compute clusters from 100 to 50,000 nodesDispatches tasks with less than 50 milliseconds scheduling overheadCoordinates up to 6.4 million parallel tasks in flightAccesses spot instances across multiple regions and providersExecutes massively parallel sweeps and simulation workloads

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