ClusterPower AI as a service

AI is powering change in every industry across the globe. We create custom made, scalable solutions, using our state-of-the-art NVIDIA AI infrastructure. Our powerful setup ensures high speed of deployment, from proof of concept to fully implemented, operational solutions.

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Focus on science and increase productivity by leveraging our AI Infrastructure and Platform as a service technology.

Benefit of an AI purpose-build infrastructure (hardware and middleware) – a turnkey solution for all types of AI workloads in one place.

Combine the latest and most powerful hardware technology, dedicated & tested architecture and the DEV-OPS tools required to get the job done faster and at optimized costs.

Gain competitive advantage through deployments of machine learning tools and technologies.

AI – Infrastructure as a Service

Cluster Power AI Infrastructure is based on the NetApp ONTAP AI integrated solution powered by NVIDIA DGX systems and NetApp cloud-connected, all-flash storage.

At the heart of ONTAP AI is the DGX A100 system, a universal building block for data center AI that supports DL training, inference, data science, and other high-performance workloads from a single platform, for every AI workload.

DGX A100 offers the unprecedented ability to deliver fine-grained allocation of computing power, using the Multi-Instance GPU (MIG) capability in the NVIDIA A100 Tensor Core GPU, which enables administrators to assign resources that are right-sized for specific workloads.

NetApp AFF systems keep data flowing to DL processes with the industry’s fastest and most flexible all-flash storage, which features the world’s first end-to-end NVMe technologies. The AFF A800 can feed data to DGX systems up to 4 times faster than competing solutions do.

 

AI – Platform as a Service

An out of the box platform as a service for AI that spins up a full-fledged ML development environment with all the tools you need at your fingertips.

Streamline data management by connecting all the necessary data sources (cloud or on-premises) and having data pipelines for automatic extraction or batch fetching in a suitable format set up for you. When configuring your ML environment, all incoming data gets automatically validated against the set parameters, and then transferred to a centralized repository.

We offer  access ready-to-use feature sets for model training, re-training, and validation.

Optimize Infrastructure Management with our custom-built platform which provides complete visibility into models’ GPU/CPU usage across and nodes and clusters. That way, our customers can continuously optimize job scheduling and resource allocation.

We also keep the data-hungry models at bay, while ensuring that other ML pipelines get access to the right amount of storage they need at the optimal speed.

Constraint-free deployment – Rely on containers or serve your models as API services using the framework you prefer — Flask, Spring, or TensorFlow.js.

 

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The power is in your hands