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Improve Performance With AI-Optimized Infrastructure

Artificial Intelligence (AI), machine learning (ML), and deep learning with neural networks allow you to use data to drive business performance. But leveraging these technologies requires specialized compute resources. Choosing the wrong approach can lead to siloed and overly complex infrastructure.

ePlus works with IT departments to implement validated architectures and packaged solutions. We build high-performance, manageable environments for AI Training and Inference at a reasonable cost. These advantages allow your team to accelerate implementations and begin leveraging the performance of technologies such as graphics processing units (GPUs). Partnering with us means your researchers and data scientists no longer have to build and manage their own computer systems or clusters or rely on expensive cloud resources.


Create a Network to Support AI and Drive Performance

We base our designs on reference architectures that reduce risk and time to value. Our priority is to create the best combination of on-premise and cloud services and resources for using data to deliver insights and business agility.

With AI-optimized infrastructure, you are able to create powerful algorithms for making better decisions faster. Data is no longer just transactions in a database or images on hard drives.

Greater efficiency enables you to handle more data even as it continues to grow exponentially. Access to structured and unstructured real-time data from sensors, devices, high-resolution videos, social media, and other systems is the key to making AI a practical tool for improving processes, workflows, and outcomes.


Leverage Proven AI Designs to Speed Implementation

By using commercially available and validated solutions, we are able to create efficient environments that are easy to implement and support. Tapping into our knowledge and experience simplifies the otherwise complex process of identifying where and how to realize business value.

This partnership enables us to deliver stable, validated hardware and software stacks for a wide range of AI workloads. It also ensures that your environment can process these workloads while remaining scalable and efficient.

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