At MDCS.AI, we understand that fragmented and uncontrolled AI workloads can lead to inefficiencies, redundancies, and security risks for your organisation. That's why we offer comprehensive AI infrastructure solutions that centralise and streamline AI workloads across your organisation. This includes a centralised AI infrastructure for managing and monitoring AI workloads and tools for optimising code, automating workflows, and scaling resources as needed. We will also provide ongoing support to help you optimise your AI workloads for maximum performance and efficiency.

AI workloads may not be well-suited for public cloud computing due to data privacy concerns, latency issues, high costs, needing specialised hardware, compliance requirements, security concerns, and scalability limitations. While (public) cloud computing can be a good option for some workloads, the unique requirements of AI workloads may make it less suitable for certain applications. We carefully evaluate your AI workloads to determine the best computing environment for their needs.

Performance is crucial for organisations that rely on AI workloads. Faster processing times can enable faster insights and decision-making, improve accuracy, enhance innovation, and lead to cost savings. That's why we offer AI infrastructure services optimised for high performance, including GPU-optimized workloads.

We review and optimise your AI workloads that (could/should) run on GPUs. This is important for improved performance, reduced costs, and enhanced scalability. GPUs are highly parallel processors requiring optimised code to leverage their processing capabilities fully, leading to faster and more efficient processing. Optimised code can help you maximise GPU utilisation, handle larger datasets, and scale their AI capabilities more effectively.