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Finding the optimal gpu nodes for a training run at the best price available.
Preparing the GPU nodes for distributed model training and deploying data & code.
Ensuring fault-tolerance, scalability, and security for a smooth model training run.
We are moving towards a future where hyper-personalized AI redefines our world. Huge AI potential lies in deep customization—tailoring models to individual needs, tastes, and skills. This is where compute becomes a critical utility, and access needs to be affordable, transparent, and open.
With Exalsius, we provide easy, affordable, and transparent access to global compute resources for AI teams by automating GPU node discovery, provisioning, model and data deployment, along with tools for deep training observation. AI teams can focus on building models; we handle the infrastructure.
To find an optimal AI training setup, we need real-time information about the current GPU market availability and pricing.
To achieve full independence from any particular GPU node provider or hyperscaler, we must set up model training environments across multiple GPU nodes from different providers.
To address the potential for node failures in distributed training environments when utilizing spot instances and cheap but volatile GPU nodes, we are implementing built-in fault tolerance.
Handling storage for large-scale datasets in distributed model training is difficult. We are building a solution that provides storage while optimizing both efficiency and network traffic costs.
