By Task

The Solution to Compute-Intensive Jobs

The Distributed Computer is at the leading Edge

Virtual CPUs, GPUs, and more to fit any workload

Greater throughput and lower unit costs

Fits any deployment model, from On-Prem to Edge

The Distributed Computer makes compute-intensive work easier and more affordable. An organization can also scale applications up and down through a single API, providing unmatched flexibility.

Leverage our enterprise-grade framework today.

Batch Processing

Stream Processing

Artificial Intelligence

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Batch Processing

The Distributed Computer enables developers, engineers, and more to run compute- and data-intensive batch processing jobs. An organization can greatly increase their parallel processing capabilities in this way. Spend less time waiting, and process greater volumes of data for less.


Whether your workloads are thousands or millions of core hours in size, scale them for a fraction of the cost.


Write once and run anywhere for the best cost and performance. The Distributed Computer is completely hardware and network agnostic.


Monitor and control your processes for situations when set and forget is not an option. Change key instructions and cancel jobs without penalty.

The Distributed Computer is ideal for massive data analysis. Whether you are finding insights about your customers or locating mineral deposits, do not let computing be a constraint.

Basic Statistics



Alignment Problems

Forecast the future or find all possibilities with vast amounts of computational infrastructure. Every model that involves some form of data parallelism is an ideal target.

Monte Carlo Simulation

Markov Chains

Stochastic & Deterministic Models

Agent-Based Models

Markov Chains

Create and analyze a wide variety of graphics, down to the smallest detail. The Distributed Computer makes large scale GPU provisioning an order of magnitude less costly.

Optical Character Recognition

Parallel & Distributed Rendering

Satellite Imagery Analysis

Facial Detection

The Distributed Computer accelerates any application with parallel elements, from cryptography to protein folding. Get in touch with our experts to learn how you may benefit.

Brute-Force Searches

Genetic Algorithms

Consensus Mechanisms

Stream Processing

Organizations are increasingly moving to a real-time, event-driven IT strategy. The Distributed Computer lets you analyze data streams at minimal cost and latency, so you can be ready for any possibility. Complex event processing is easier than ever.

Pre-process close to the edge and discard low-value data.

Notify key decision makers when triggers are met.

Automatically scale up processing capability during peak times.

Route higher-value workloads to the cloud, saving bandwidth cost.

Implement ML systems that analyze live data streams.

Artificial Intelligence

Artificial Intelligence is a revolutionary technology that is increasingly limited by the scale of required computing resources.

The Distributed Computer significantly improves the training and deployment of machine learning applications. All three categories of ML are ripe for disruption with its low-cost computing and highly flexible networks.

Supervised Learning

The Distributed Computer provides low-cost, high-availability GPUs for even the most complex training jobs. From standard regression models to convolutional neural networks, use the platform your team needs to drive previously unobtainable results.

  • Parallel Batch Processing
  • Asynchronous SGD

Unsupervised Learning

Several massively parallel techniques can be carried over from supervised learning. This makes the classification of unlabeled data faster and less costly with the Distributed Computer.

  • Massively Parallel Hyperparameter Search
  • Mixture of Experts Training

Reinforcement Learning

Intelligent systems that make use of agent-based models benefit significantly from the Distributed Computer. With far more compute and memory resources, these systems can explore a greater state space in less time.

  • Q-Learning & Deep-Q Learning
  • Experience Based Learning

Additional Benefits

Promoting Visibility

There is a significant push for greater visibility into model decision making. With retraining and interacting with even complex models made simple, the Distributed Computer is an important part of developing transparent AI.

The Future of Federated

The Distributed Computer enhances the capabilities of edge ML while preserving privacy. Completely hardware agnostic, the platform can build robust models with local or even solely on-device compute.

Don't See Your Use Case?

If your use case is not listed, please reach out to our team of experts. The Distributed Computer is always finding new applications.

Get in Touch

Contact us today to get started with our public cloud services. Alternatively, we are here to help you decide if another deployment model is right for you.