Zibralabs provides a platform for building large‑scale distributed compute clusters that leverage the lowest‑cost CPUs and GPUs across hyperscalers and emerging “neocloud” providers. The system is designed for AI workloads, enabling massively parallel tasks such as backtesting, reinforcement‑learning pipelines, multi‑modal data processing, and high‑throughput inference across tens of thousands of nodes. Customers can scale clusters from 100 up to 50,000 nodes to run any parallel compute job efficiently.
Funding
Funding not disclosed
Founders
Product
Problem
AI researchers and engineers often need to run massive parallel compute jobs, but accessing large numbers of low‑cost CPUs and GPUs across multiple cloud providers is complex and expensive. Managing spot instances, heterogeneous hardware, and cross‑region scheduling adds operational overhead, limiting the scale and speed of workloads such as backtesting, reinforcement learning, and high‑throughput inference.
Solution
Zibralabs offers a platform that automatically assembles distributed compute clusters from the cheapest available CPUs and GPUs across major hyperscalers and emerging neocloud providers. The system handles spot‑instance procurement, cross‑region resource allocation, and low‑latency dispatch, enabling clusters that scale from 100 up to 50,000 nodes with sub‑50 ms scheduling overhead. Users can submit any massively parallel AI workload—parameter sweeps, RL pipelines, multimodal data processing, or batch inference—and the platform orchestrates execution while optimizing cost and performance. Results are aggregated and returned through a unified interface, allowing teams to focus on model development rather than infrastructure management.
Target Audience
Primary customers are AI teams, data science groups, and research labs that require large‑scale parallel compute for model training, simulation, or inference, particularly those seeking cost‑effective cloud infrastructure.
Features
- Automated aggregation of low‑cost CPU and GPU resources from multiple cloud providers, including spot instances
- Scheduler optimized for sub‑50 ms dispatch and minimal overhead across thousands of nodes
- Support for heterogeneous clusters (CPU + GPU) that can span multiple regions
- Built‑in workload templates for backtesting, large‑parameter sweeps, reinforcement‑learning rollouts, multimodal pipelines, and high‑throughput inference
- Dynamic scaling from 100 to 50,000 nodes based on workload demand
- Unified monitoring and reporting dashboard for job status, cost tracking, and performance metrics