The Future of AI: Inference Unleashed

The Rise of AI Inference and Its Impact on Businesses

Every second, millions of AI models across the world are processing loan applications, detecting fraudulent transactions, and diagnosing medical conditions, generating billions in business value. Yet, most organizations are still struggling to bridge the gap between AI experimentation and production systems that deliver measurable returns. For African businesses, this represents both a significant challenge and an unprecedented opportunity to leapfrog traditional limitations.

Since early 2023, companies globally began building, training, and customizing foundational AI models, laying the groundwork for transformative solutions. Today, the conversation has shifted from model training to deployment. The real question now is: how do we get models to work efficiently and at scale in production?

“It’s not just about AI models. It’s about having the right model and the right data—your proprietary data—that’s adding value to your business,” said Red Hat CEO Matt Hicks.

This shift has led to a surge in demand for AI inference, the process of running both traditional and generative AI models in real-world environments to deliver insights, automation, and decision-making. AMD forecasts that inference demand will grow by over 80% annually, highlighting the transition from experimentation to practical implementation. Africa is also responding to this trend, with South Africa now home to two data centers capable of training AI, and two inference-ready facilities.

The next frontier in enterprise AI is no longer about building the biggest model—it’s about running the right model efficiently to deliver business value.

Where and How AI Models Deliver Value

AI inference is the operational phase where trained models generate predictions or content in response to real-world inputs effectively, the point at which AI transitions from development to production deployment. Unlike training (which is akin to teaching the model), inference is the “runtime” phase that IT and development teams manage daily. This is where businesses see tangible returns on their AI investments.

Examples of AI inference delivering value across key sectors:

• Healthcare: Real-time analysis of patient data, medical imaging, and diagnostic support similar to querying a specialist medical database, but with advanced pattern recognition. Modern AI systems can process medical images in under 500ms, compared to traditional methods that might take hours.
• Financial services: Live transaction monitoring for fraud detection, processing thousands of payments per second with sub-100ms response times. Optimized inference systems can analyze transaction patterns and flag anomalies in under 100ms, helping prevent fraud while maintaining a seamless customer experience.
• Telecommunications: Continuous network performance analysis and predictive maintenance akin to distributed system monitoring, but with AI-powered anomaly detection. These systems can process network data streams in real time, identifying potential outages 30–60 minutes before they occur.

Technical and Operational Considerations

Key challenges include:

• Managing latency requirements (often sub-100ms for real-time applications)
• Computational costs that scale with usage
• Infrastructure demands for specialized hardware (GPUs, TPUs)
• Efficiently serving large models in continuous production environments

Unlike training, which happens periodically, inference runs continuously and often becomes the most resource-intensive phase of the AI lifecycle. This poses particular difficulties for organizations with limited infrastructure, as inference requires sustained high-performance computing—not the burst compute patterns typical of traditional workloads.

Achieving Efficiency Through Model Optimization

Enterprises are increasingly adopting small language models (SLMs) to balance performance with operational efficiency. SLMs are easier to fine-tune, faster to deploy, and significantly more cost-effective than massive LLMs. By tailoring models to specific use cases and further refining them through quantization, distillation, or domain-specific fine-tuning, businesses can achieve substantial gains:

• Response-time optimization: Reduced from 2–3 seconds to under 200ms for most business applications
• Speed improvements: Quantized models achieve 2–4× faster inference with minimal (under 2%) accuracy loss
• Cost reduction: SLMs can lower inference costs by 60–80% while maintaining performance
• Resource efficiency: Properly optimized models reduce GPU memory requirements by up to 50%, enabling deployment on more affordable hardware

Making AI Inference Work for Africa

African businesses should adopt the following best practices:

• Right-size the model: Smaller, task-optimized models often deliver 3–5× better price-performance ratios than oversized ones.
• Align model with use case: Task-specific models typically offer 40–60% better performance than general-purpose alternatives.
• Plan deployment strategy: Decide whether inference should run on-premises, in the cloud, or at the edge depending on latency, data sovereignty, and infrastructure availability. Edge deployments can reduce latency by 70–90% for real-time workloads.
• Contextual model tuning: Refine models using domain-specific terminology, tone, and compliance needs. This improves performance for retrieval-augmented generation (RAG) pipelines by 15–25%.

Foundational Models and Enterprise-Grade Inference

The rise of pre-trained, general-purpose foundational models—many of which are open source—has accelerated enterprise AI adoption. These models can be downloaded, quantized, and deployed quickly, reducing time to value and lowering entry barriers for businesses.

Vendors such as Red Hat now offer platforms to streamline and optimize foundational model deployment. The Red Hat AI Inference Server is a scalable, air-gapped, and cloud-agnostic solution for efficient, production-grade inference. Built on open-source technologies like vLLM, it supports most GenAI models, offering maximum flexibility and rapid innovation.

Unlocking Innovation, Optimization, and Adaptability

Inference is not a one-off task; it’s a continuous operational workload. Organizations must therefore plan for:

• Scalable infrastructure: Systems capable of handling 10–100× traffic spikes during peak usage
• Model orchestration: Platforms to manage and chain multiple models, reducing processing time by 30–50%
• Performance monitoring: Real-time tracking of latency, throughput, and resource utilization, with automated alerts
• Ongoing optimization: Continuous refinement via retraining and performance tuning can yield 20–30% annual efficiency gains

As inference complexity grows, so does the need for robust model selection, evaluation, and lifecycle management. The demand for fast, scalable inference will continue to rise, especially with the emergence of Agentic AI.

Agentic AI builds on today’s capabilities by chaining reasoning models with task-focused SLMs and enterprise-contextual data, dramatically increasing the need for efficient inference. This is already evident in Africa, where Absa is among the first financial institutions globally to offer agentic AI services to its customers.

The real business value of AI is not unlocked when models are trained, but when they are running reliably, efficiently, and cost-effectively in production. That is the promise of AI inference, and Africa is uniquely positioned to lead through innovation, optimization, and adaptability.

By focusing on right-sized models, efficient deployment, and continuous improvement, African enterprises can go beyond the hype and extract real, measurable value from AI, achieving up to 2–4× performance gains while reducing costs by up to 80% compared to traditional approaches.