Today Large Language Models (LLM) and other big Machine Learning (ML) models take the upfront of the stage. These models can now be trained for specific, customized solutions. But running these models, doing inference on a new dataset, still requires access to a big datacenter.

What if a company or an organization doesn't have access to a datacenter, or if the input data is too confidential? We propose to run the inference on on-premise servers. This keeps data secure.

Our Solution

We fully automate and optimize the distributed deployment of ML models for training and inference, dynamically leveraging on-premise servers. Our high-performance, secure solution is ideal for companies seeking local ML usage with sovereignty and scalability.

• Simplicity – Clients can focus on their business applications while our solution transparently handles distributed deployment.
• Efficiency – Clients can utilize existing machines, maximizing available computing power—even across heterogeneous hardware.
• Scalability – Large models can be run locally
• Privacy – Our solution enables organizations to leverage AI's power locally without relying on untrusted providers.