Article: Lonwabo Mtyeku – Community Newsroom Photo Credit: Supplied
Artificial intelligence is no longer on the horizon — it is here, rapidly redefining how industries operate, how organisations innovate, and how economies compete. Yet for South Africa, a country where energy supply remains constrained and digital infrastructure uneven, the explosive rise of large language models (LLMs) brings with it a stark reality: bigger is not always better.
The global race toward ever-larger models has dazzled the world, but in South Africa, sustainability, efficiency, and accessibility must guide the next phase of AI adoption. Encouragingly, new technological pathways — particularly Small Language Models (SLMs) and open-weight architectures — are proving that high performance does not require high consumption.
A Regulatory Landscape Signalling Real Change
South Africa has not yet imposed hard legislative limits on data centre energy usage, unlike the EU and the United States. But the trajectory is clear. The National Data and Cloud Policy (2024) has begun shifting expectations, encouraging data centre operators to adopt independent backup power and energy-efficient cooling systems. These proposals reflect a broader national priority: strengthening the reliability and sustainability of digital infrastructure.
With the forthcoming National AI Policy and potential AI Act, efficiency and responsible innovation are expected to move from “nice to have” to “non-negotiable.”
Innovation in a Resource-Constrained World
For South African developers, engineers, and business leaders, the message is unmistakable: AI innovation must be redesigned for a world where energy is limited, costs are rising, and infrastructure varies dramatically across regions.
This is where SLMs — small, smart and surgically precise — come into their own.
SLMs, typically under 10 billion parameters, require only a fraction of the computational and energy resources demanded by LLMs with hundreds of billions or even trillions of parameters. Despite their size, they deliver remarkable performance when trained and deployed correctly — and with significantly lower environmental and financial impact.
Their advantages are undeniable:
- Run efficiently on modest hardware
- Deliver ultra-low latency
- Reduce energy consumption
- Deploy locally in remote or resource-limited environments
- Enable inclusive innovation across more sectors and regions
This makes SLMs ideal for South Africa’s ambitions for digital transformation, especially in education, agriculture, healthcare, and finance.
The Myths Have Fallen Away
For years, organisations assumed SLMs lacked enterprise-grade power — that they were too narrow, too weak, too limited for real-world complexity.
The last 18 months have changed everything.
Breakthroughs in model architecture and — crucially — in data efficiency have proven that performance is not proportional to size. LLMs rely on massive, often unfiltered datasets containing duplicated, noisy or irrelevant content. SLMs, however, excel with smaller, expertly curated datasets designed for precision.
What we’ve learned:
- Quality beats quantity
- Domain-specific training drives accuracy
- Smaller models can be fine-tuned faster and updated more frequently
- They thrive in dynamic, real-time environments
This makes SLMs a natural fit for industries requiring speed, accuracy, and strong contextual awareness, such as law, medicine and financial services. Pilot projects in South Africa already show SLMs delivering faster inference, reduced latency and smaller hardware footprints — all while maintaining high accuracy.
Education and Agriculture: Two Sectors Poised for Leapfrog Growth
SLMs hold transformational potential for South Africa’s most critical development sectors:
1. Higher Education
With customised, course-aligned training data, SLMs can:
- Support teaching and tutoring
- Enhance research capabilities
- Enable adaptive learning
- Create language-specific or subject-specific academic tools
- Reduce pressure on already-strained IT systems
2. Agriculture
By adapting globally trained models with local knowledge — soil conditions, climate patterns, crop varieties — SLMs can help farmers:
- Improve yields
- Predict disease outbreaks
- Optimise resources
- Enhance sustainability
By focusing on local context, developers can create deeply relevant, high-impact solutions without the overhead of training massive new models from scratch.
Open-Weight Models: A Second Path to Energy-Efficient AI
Alongside SLMs, open-weight models — particularly those using Mixture of Experts (MoE) architectures — offer another compelling alternative.
MoE models activate only a small percentage of their parameters for each query. A model with 100 billion parameters might only need 5 billion at inference, drastically cutting compute and energy usage.
Benefits include:
- High scalability
- Lower energy consumption
- Customisability for local or enterprise-specific use cases
- Suitability for edge computing environments
These models demonstrate that organisations no longer need massive cloud infrastructure to access advanced AI tools. Localised deployment becomes possible — and practical.
Efficiency Must Become a Core Principle of AI Strategy
As AI evolves, the next wave will be defined not by the pursuit of size but by the pursuit of intelligence per kilowatt.
To truly unlock AI’s potential in South Africa, organisations must embed efficiency at every level:
- Data curation — prioritise quality, relevance and ethics
- Model architecture — favour models optimised for performance, not scale
- Deployment — adopt hybrid and edge-friendly strategies
- Operations — measure and manage energy usage across the AI lifecycle
Holistic, lifecycle thinking will be essential for developers and enterprises aiming to innovate responsibly.
South Africa’s Moment: Small Models, Big Impact
The global AI race is shifting. The era of “bigger at all costs” is giving way to a smarter, more sustainable approach — one that prioritises accessibility, efficiency and purpose.
For South Africa, this is an opportunity.
By championing SLMs and energy-efficient architectures, the country can:
- Innovate without overwhelming its power grid
- Expand digital inclusion
- Build specialised, high-performing AI tools
- Strengthen competitiveness across industries
- Position itself as a global leader in responsible AI
The future of AI will not be defined by who builds the biggest model — but by who builds the most efficient, effective and accessible systems for their people.
South Africa can lead this future.
