September 9, 08:49TechCentral

Rethinking Artificial Intelligence Infrastructure for South Africa with Compact Solutions

South African organizations have moved past questioning the potential of AI; the focus has shifted to operational tasks: how to implement AI, where it should operate, how much computing power will be required, and how this will affect data, budgets, and overall technological strategy.

Previously, AI implementation was viewed primarily as a software issue—selecting a platform, a training model, and priority use cases. However, hardware components have become the bottleneck: GPUs, memory, storage, networking equipment, power and cooling systems, security, and data management. Access to the necessary computing power has become as critical as access to a suitable model.

Infrastructure Situation in South Africa

Local companies face several specific limitations. High-performance computing is expensive, and access to advanced GPU infrastructure is limited. The performance of cloud AI suffers due to bandwidth and latency issues. Large-scale planning is complicated by energy supply and data center capacity concerns: less than half a gigawatt of active data center capacity exists in Africa per billion people. Furthermore, data sovereignty issues add complexity for those working with regulated information, and skilled specialists in specialized AI infrastructure are scarce.

South Africa cannot simply copy hyperscale models created in other regions. The assumption that every organization must have a large data center or significant commitments to cloud services is no longer viable or necessary.

A Huge Data Centre Is Not Always Needed

As AI adoption matures, workloads are becoming more distributed. Developers require computational resources for testing and training models, while business units need local inference for analytics, automation, and computer vision. Operational environments, whether factories or retail stores, increasingly rely on edge AI for real-time data processing.

This opens up opportunities for compact, distributed infrastructure. Instead of centralizing computation in one data center or cloud environment, organizations can deploy small, AI-capable systems at various business points. This provides significant computing power without the costs and timelines associated with building a traditional AI data center.

The Rise of Accessible AI Compute

To meet this need, a new generation of compact systems is emerging, designed to bring AI computing power closer to users, developers, and operational environments. This allows organizations to start small, conduct local experiments, and scale as real demand grows.

An example is the ASUS Ascent GX10. It is built on Nvidia's GB10 Grace Blackwell superchip—the same silicon used in Nvidia's DGX Spark. This system delivers AI performance of petaflop FP4 and 128GB of unified memory in a 150x150x51mm chassis, sufficient for fine-tuning models up to 200 billion parameters right on a desk. Two such units can be connected via the ConnectX-7 network to double performance. At the same time, the power consumption reaches 180W, which is important in a market where power supply is a limiting factor in planning, not just an expense item.

The ASUS ExpertCenter Pro ET900N G3 provides workstation performance for business and professional needs, supporting AI workloads that need to be closer to the user in fields like engineering, analytics, design, research, or operations. For many organizations, this is a practical compromise: the system is powerful enough for complex AI tasks yet accessible for deployment across different teams and departments.

The practical effect of such systems is local development and testing, accelerated experimentation cycles, bringing data closer to the point of use, tighter control over sensitive tasks, reduced reliance on cloud connectivity, and creating a realistic starting point for an organization building its own AI capability.

It Is No Longer Cloud Versus On-Premises

For years, infrastructure decisions boiled down to a binary choice: cloud or on-premises. Artificial intelligence is disrupting this paradigm because different workloads have different requirements, and these requirements determine where they should run.

Some AI tasks benefit from the elasticity and scale of cloud services. Others require low latency, local processing, data sovereignty compliance, predictable costs, or autonomous operation capabilities, pointing to the need for local or edge infrastructure.

Thus, the future is hybrid. Organizations will combine cloud, on-premises, and edge computing based on the nature of each workload, and compact systems like the GX10 and ET900N will become part of this model, allowing computation to be placed exactly where it is needed. The question is which location suits which workload.

From Proof of Concept to Production

Many technically successful concepts fail at the operational level because infrastructure planning was treated as a secondary concern. Overcoming this gap requires assessing current and future workloads, separating training from inference, considering GPU and memory requirements, data location, applicable sovereignty rules, security concerns, power consumption, cooling, network latency, total cost of ownership, and environmental scalability.

This assessment must be integrated into the organization's AI strategy, not kept alongside it. Infrastructure defines the limits of possibility, the speed of AI deployment, and the economic sustainability of its operation.

The Role of the Technology Ecosystem

Making AI accessible is more than just selling hardware. It requires building an entire ecosystem that includes computing power, infrastructure, software, cybersecurity, distribution, technical expertise, and channel partners.

Altron Arrow, which distributes ASUS AI systems in South Africa, is part of this ecosystem, helping organizations and partners gain access to what is needed for modern AI deployments. The goal is to provide computing power that is realistic, scalable, and economically viable for local organizations, rather than just a scaled-down copy of a hyperscale solution.

Accessibility will define the next phase of AI adoption. The greatest benefit will go to organizations that understand their workloads and build their infrastructure strategy around them, not necessarily those with the largest budgets. For South African businesses, the barrier of accessibility is no longer an obstacle; the current focus is on how quickly AI moves from experiment to operational advantage.

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yourstory.comAugust 27, 13:49

How Agentic AI is Transforming Startups by Turning Data into Autonomous Solutions

At the Snowflake Startup Mixer held on July 17th in Hyderabad, founders of startups, technology leaders, and investors gathered. The central theme of the event was transforming data into decision-making and subsequent actions. During the panel discussion, product demonstrations, and networking, it was discussed how startups are moving beyond simple data collection and analysis to create intelligent systems capable of making autonomous decisions using agentic AI.

A panel session titled 'Startups That Work Around the Clock: Building Companies That Never Sleep' was moderated by Shivani Mutanna, Senior Director of Content Partnerships at YourStory. Participants included Utkarsh Sharma, Associate Creative Director at Pocket FM; Kishor Indukuri, Founder and CEO of Sid’s Farm; Abhishek Deshpande, COO and Co-founder of Recykal; and Kiran Kalluri, Partner at Dallas Venture Capital.

The discussion began with the question of what 'working around the clock' truly means. For the participants, it did not mean employees working 24 hours a day, but rather creating systems that continue to execute critical workflows even after teams have finished their work.

In the case of Pocket FM, which serves an audience across different countries, content production never actually stops. AI has become an integral assistant throughout the entire creative process, accelerating production while maintaining quality. The company uses AI for material generation, asset processing, and production support, while humans remain responsible for quality and narrative.

A similar concept is applied at Sid’s Farm. With thousands of daily deliveries, raw material movements, and customer interactions, data continuously flows from farms and production sites into logistics networks and to consumers. Indukuri explained that AI helps the company interpret this data quickly enough to react before minor operational issues escalate into more serious ones.

At Recykal, the workday begins before dawn when waste collectors start their routes and continues late into the evening as loads move through the city. Every stage generates operational data, providing opportunities to optimize decisions in a supply chain that rarely stops.

From an investor's perspective, Kalluri noted that the key difference lies between companies that merely use AI tools and those fundamentally built around AI-supported operations. If removing AI leaves workflows largely unchanged, the company is primarily using AI for efficiency gains. However, if the absence of AI requires a complete overhaul of the business itself, then the organization can be considered truly AI-native.

As AI integrates deeper into business processes, startups are also rethinking scaling. For Recykal, AI ceased to be viewed as a separate technological initiative and became a corporate requirement. Deshpande stated, 'We have an internal mandate. Every Monday there must be a new AI initiative. We must discuss it, and anyone can propose it, from an intern to a CXO.'

He explained that the company mapped every business function, identified repetitive processes suitable for AI, and used years of operational data to redesign workflows. The results were significant: Recykal increased annual revenue from 400–500 crore to 1,400 crore while growing its staff from approximately 80 to 128 employees.

At Pocket FM, Sharma emphasized that management positioned AI as a support tool, not a replacement for creative specialists. The company invested in AI-powered audio, video, and image generation tools while ensuring that humans continued to perform quality checks and maintain the emotional experience expected by viewers. Creating stories consisting of hundreds or even thousands of episodes still requires human judgment, with AI accelerating production rather than replacing creativity.

At Sid’s Farm, AI implementation efforts are focused on consumer-facing operations. Indukuri noted, 'Where we actively use AI, or the area we constantly monitor, is the consumer side.' The dairy industry generates a massive amount of operational data daily. AI increasingly helps forecast demand, reduce waste, optimize production planning, and dynamically adjust inventory across delivery channels.

Kalluri added that these examples demonstrate what distinguishes enduring companies that use AI from enterprises that merely wrap existing language models. He warned, 'If there is no ability to capture these processes, those are red flags we see. And those are not companies that will be able to scale and grow. They might succeed with one specific client in a very niche case, but if they need to expand their business and attack the entire TAM, it becomes very difficult if they lack these foundations.'

For investors, stronger indicators of long-term scalability are owning differentiated data, building robust workflows, and embedding AI into all products, go-to-market functions, and internal operations, rather than just branding the company as AI-oriented.

As startups automate more workflows, the question of where AI should end and where humans must retain control becomes increasingly important. The panelists agreed that while AI can significantly accelerate task execution, critical business decisions still require human oversight.

Deshpande cautioned against accepting AI outputs as infallible. 'I feel that AI is a 'yes-giver,' so we must be very smart. Human intervention is needed. You cannot replace people. Fundamentally, you must clearly understand what you want.'

Indukuri reported that Sid’s Farm integrates AI into recruitment, customer support, and operational planning, but believes AI should first handle routine queries before escalating complex situations to humans. For example, customer support can automatically answer questions about delivery or order status, while dissatisfied customers or quality issues should be routed to human teams. Instead of replacing employees, AI allows them to focus on problems requiring judgment, empathy, and context.

The discussion also highlighted that technology alone is insufficient to define startup success. Founders need disciplined organizations, reliable processes, high-quality data, and a clear understanding of where AI creates real business value. Deshpande observed that AI is already changing the approach startups take to talent: 'I have seen an intern deliver better results than someone with 10 years of experience. The younger generation adapts faster.'

He added that founders themselves must become active users of AI before expecting their teams to adopt new tools. Success depends on defining the right business problems first; only then can AI yield meaningful results instead of adding unnecessary complexity.

The evening concluded with a live demonstration titled 'Blueprint: From Data to Action with Agentic Workflows,' presented by Akshat Parik, Harish Chintakunte, and Navedea Odja from Snowflake. The session demonstrated how startups can significantly reduce the time required to build AI-based applications by combining enterprise data with agentic AI capabilities. Using Snowflake Cortex Code, a specialized AI coding agent for data workloads and AI, the speakers showed how developers can move from a natural language prompt to a production-ready application in minutes.

A use case example for fraud detection in the fintech sector illustrated this process. One prompt generated an application that ingested customer and transaction data, calculated fraud risk scores, created dashboards, and formed an AI-driven investigation workspace. In addition to dashboards, users could ask questions about fraud trends, customer behavior, and transaction patterns in natural language. AI broke down these queries into multiple reasoning steps before generating actionable insights.

The team also highlighted the flexibility of Snowflake models. Instead of locking organizations into one base model, the platform supports several leading models, including Claude, Llama, DeepSeek, Mistral, and OpenAI, allowing businesses to choose the appropriate model for each task while keeping data within a secure governance system. One message remained clear throughout the evening: AI is no longer just helping startups work faster. It is increasingly becoming part of how enterprises are designed and function. Startups are beginning to embed AI into the core of their products, workflows, and daily decision-making, rather than viewing it as a standalone tool.