By: Dr Prince Abudu - Head of Technology, Cassava AI & Charlotte Awino, AI Engineer, Cassava AI

As Africa increasingly leans towards the local development of artificial intelligence (AI) solutions, one thing has become clear: we need enormous computing power. From preparing datasets to training, testing, deploying, and monitoring models, every stage of AI depends on access to high-performance infrastructure. Until recently, African innovators had to rely on overseas cloud services or invest in prohibitively expensive hardware. The arrival of GPU-as-a-Service (GPUaaS) changes that equation.

By delivering advanced compute capacity directly from African data centres, GPUaaS enables developers, researchers, and enterprises to build and deploy AI solutions locally, more cost-effectively, and with greater sovereignty. For the first time, innovators can develop AI-driven solutions to address uniquely African challenges – in African languages and using African data – without sending information offshore.

How the service works

At its core, GPU-as-a-Service is about simplifying access to AI computing. The infrastructure forms the foundation, providing the high-performance GPUs and CPUs needed to train AI. On top of that sits the platform, a managed environment where developers can build and test their applications without worrying about the underlying hardware. The next layer is models. These are ready-made or customised AI tools that people can plug into their work. Finally, the software layer turns these models into complete applications for organisations that want ready-to-use solutions. This setup allows different groups, including researchers, start-ups, and governments, to get involved at the level that best suits their skills and needs.

Why it matters for business

Understandably, African business leaders may be sceptical about the value of these facilities. However, the benefits speak for themselves. First, efficiency. Reducing the time to train and deploy models speeds up the decision-making cycle. Then, cost savings. Instead of purchasing GPUs outright, organisations pay only for what they use. And finally, data sovereignty and compliance. With compute infrastructure based locally, data remains within jurisdictional borders, which is particularly critical for industries like financial services, healthcare, and the public sector.

They could also be concerned about losing the human element of development. The reality is that human intelligence plays an essential role in ensuring that AI outputs are contextually relevant, ethically sound, and aligned with local priorities. This is particularly true for Africa, where a model trained on European or American datasets is unlikely to yield results that are appropriate for our contexts. This is why human review, dataset curation, and governance are built into the process.

Building local expertise and infrastructure

Previously, African organisations had little choice but to depend on global cloud providers located far from the continent. GPUaaS closes that gap. And while readiness varies across the continent, what is consistent is a growing appetite to apply AI in ways that improve country- and region-specific difficulties in areas such as public services, healthcare, agriculture, and financial oversight.

We need to remember that infrastructure alone can’t transform Africa’s AI landscape. It’s the partnerships behind the infrastructure that unlock true value. Collaborations such as Cassava Technologies’ work with NVIDIA demonstrate how global expertise and local commitment combine to provide African organisations with cutting-edge tools. The outcome is not simply more computing power, but a stronger foundation for African-led innovation.

Home-grown solutions to real issues

On-demand GPU capacity is emerging as a cornerstone of Africa’s digital backbone, helping to create solutions to some of Africa’s most pressing challenges. With GPUaaS, Africa is no longer limited to consuming global AI solutions. Instead, the continent is building the capacity to design its own. This could be analysing health records securely on local infrastructure or using document classification models to process tax forms more effectively. It could be helping authorities deploy analytics that improve road safety and traffic flow, or researchers at African universities running complex experiments without sending data overseas.

Regardless of the country, one thing is clear: with the proper infrastructure, expertise, computing power, and innovative mindset, Africa has the potential to be not only a participant but a creator in the global AI economy.