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Introduction: The AI Race Is No Longer About Building Better Models, It Is About Powering Them
Artificial intelligence has entered a completely different phase. The industry is no longer focused solely on creating larger language models or discovering smarter algorithms. Instead, the real competition is shifting toward something far more demanding, delivering AI services to millions of users every second without interruption.
Behind every chatbot response, AI-generated image, coding assistant, and autonomous agent lies an enormous amount of computing power. The challenge is no longer whether AI models are intelligent enough. The challenge is whether companies can afford to run them continuously.
NVIDIA has recognized this transformation before much of the industry. Rather than simply selling GPUs, the company is now introducing an entirely new business strategy designed to reshape how AI infrastructure is financed, deployed, and consumed. Its latest initiative could significantly reduce the barriers preventing startups, enterprises, and regional AI providers from accessing world-class accelerated computing.
The result is a new generation of AI factories built specifically for manufacturing tokens at industrial scale, bringing cloud computing into an entirely new economic model.
AI Has Entered the Age of Continuous Inference
During the early AI boom, most investments focused on training massive foundation models. Once these models were completed, attention quickly shifted toward something equally demanding: inference.
Inference is the process of actually serving AI responses to users in real time. Unlike training, which happens occasionally, inference runs continuously, often 24 hours a day.
Every generated token consumes computing resources.
Every conversation requires GPU power.
Every enterprise AI workflow increases infrastructure demand.
As businesses increasingly integrate AI into customer service, software development, research, healthcare, finance, and automation, inference workloads continue expanding at an unprecedented pace.
This growing demand has created an entirely new infrastructure challenge.
Traditional Infrastructure Financing Has Become a Major Obstacle
Large AI infrastructure projects require billions of dollars in investment.
Building data centers involves securing land, obtaining power contracts, constructing facilities, purchasing thousands of GPUs, networking hardware, cooling systems, and operational staff.
Historically, only hyperscalers and the
Even promising AI startups with strong long-term contracts frequently struggled to obtain financing because infrastructure investments remained too expensive and risky.
This financial bottleneck has slowed AI innovation across much of the emerging ecosystem.
NVIDIA Introduces a Completely New Business Model
Rather than limiting itself to hardware sales, NVIDIA is changing the financial structure of AI infrastructure itself.
Its new strategy enables AI cloud providers to gain access to massive NVIDIA-powered infrastructure using a revenue-sharing and credit-supported model.
Instead of requiring enormous upfront capital expenditures, cloud providers can scale infrastructure while aligning their financial success directly with customer usage.
This creates incentives for every participant.
NVIDIA benefits from recurring usage-based revenue.
Cloud providers gain easier access to world-class infrastructure.
Customers receive faster access to AI compute resources.
The model transforms GPUs from one-time hardware purchases into long-term infrastructure partnerships.
NVIDIA DSX AI Factories Become the Centerpiece
At the heart of the initiative are NVIDIA DSX AI factories.
Unlike traditional data centers, these facilities are optimized specifically for manufacturing AI tokens at industrial scale.
Every layer is designed around AI workloads.
Accelerated networking.
High-density GPU clusters.
Optimized software stacks.
Enterprise-grade orchestration.
Multi-tenant resource allocation.
These AI factories function much like manufacturing plants, except instead of producing physical goods, they continuously generate computational intelligence.
The more efficiently they produce AI tokens, the greater their economic value.
Faster AI Deployment Without Years of Waiting
One of the biggest advantages of
Traditionally, organizations seeking large AI clusters often faced months or even years of delays while waiting for land acquisition, electrical infrastructure, construction, hardware installation, and software deployment.
Under the new model, companies can access existing NVIDIA-powered AI factories much more rapidly.
This dramatically shortens deployment timelines for:
AI startups
Foundation model developers
Enterprise AI teams
Research institutions
Government AI initiatives
Regional cloud providers
Instead of building infrastructure from scratch, organizations gain access to production-ready compute environments.
Sharon AI Plans Massive GPU Deployment
One of the earliest participants in
The company plans to deploy up to 40,000 NVIDIA Grace Blackwell GB300 GPUs, creating one of the largest dedicated AI computing environments in the ecosystem.
According to CEO James Manning, the collaboration represents a major milestone in building sovereign AI infrastructure capable of supporting large-scale national and enterprise AI initiatives.
Such deployments reflect a growing trend toward regional AI independence, where countries and organizations seek local infrastructure instead of relying entirely on foreign hyperscalers.
Firmus Builds One of Southeast
Another major participant is Firmus Technologies.
The company is constructing a massive NVIDIA DSX AI factory campus in Batam, Indonesia.
Once fully completed, the facility is expected to reach approximately 360 megawatts of computing capacity while supporting as many as 170,000 NVIDIA GPUs.
That scale places the project among the most ambitious AI infrastructure developments currently underway.
Its primary goal is providing energy-efficient and economically scalable AI cloud services for enterprises throughout the region.
AI-Native Companies Are Driving Compute Demand
The companies placing the greatest pressure on AI infrastructure are those whose entire businesses depend on artificial intelligence.
Organizations including Baseten, Fireworks AI, and Together AI require enormous GPU capacity across multiple workloads.
These include:
Foundation model training
Post-training optimization
Fine-tuning specialized models
Agentic AI systems
Enterprise inference
Developer APIs
Production-scale deployment
Their customers expect reliable performance regardless of traffic spikes.
Meeting these expectations requires infrastructure capable of scaling dynamically without sacrificing availability.
The Economics of AI Are Rapidly Changing
NVIDIA’s strategy reflects a broader transformation in AI economics.
In the past, hardware manufacturers primarily generated revenue from selling physical products.
Increasingly, AI infrastructure is becoming a service economy.
Recurring revenue tied directly to compute usage provides greater financial predictability for infrastructure providers while reducing risk for cloud operators.
This creates a more sustainable ecosystem where both hardware vendors and cloud providers grow alongside customer demand.
It also lowers the barrier for startups entering highly competitive AI markets.
Regional AI Infrastructure Could Become the Next Competitive Advantage
Many governments and enterprises have begun prioritizing sovereign AI infrastructure.
Keeping data processing inside national borders improves regulatory compliance, strengthens privacy protections, reduces latency, and enhances strategic independence.
NVIDIA’s distributed AI factory approach supports this trend by allowing regional cloud providers to deploy advanced infrastructure without independently financing enormous GPU purchases.
Over time, this could produce a globally distributed AI ecosystem rather than concentrating compute capacity within only a handful of hyperscale providers.
What This Means for the Future of AI
The importance of AI infrastructure is becoming comparable to electricity during the Industrial Revolution.
Every intelligent application depends on reliable computational power.
Companies capable of delivering scalable inference will increasingly dominate the next generation of software.
NVIDIA is positioning itself not simply as the world’s leading GPU manufacturer, but as the operating backbone of industrial-scale artificial intelligence.
If successful, its AI factory strategy may become one of the most significant infrastructure shifts since the birth of cloud computing itself.
What Undercode Say:
The most important aspect of
The company understands that GPU sales alone eventually become cyclical.
Recurring compute revenue creates significantly greater long-term stability.
This resembles the historical transition from perpetual software licenses toward Software-as-a-Service.
Infrastructure itself is becoming subscription-driven.
The term “AI Factory” is also strategically important.
It reframes GPUs from components into production assets.
Tokens become measurable industrial output.
That makes AI infrastructure easier to finance, benchmark, and monetize.
Revenue-sharing also lowers investment risk for cloud operators.
Instead of purchasing thousands of GPUs outright, providers can align payments with customer demand.
This reduces idle infrastructure.
Higher utilization improves profitability.
It also helps NVIDIA keep GPUs active rather than sitting unused.
Another overlooked implication is market decentralization.
Large hyperscalers have traditionally controlled AI infrastructure.
This initiative gives regional providers an opportunity to compete.
Countries seeking sovereign AI capabilities may find this model particularly attractive.
Energy efficiency will become increasingly critical.
Future AI competitiveness may depend less on raw GPU count and more on performance per watt.
Facilities consuming hundreds of megawatts must justify every unit of electricity.
Software optimization becomes equally important.
High GPU utilization often delivers larger returns than simply adding more hardware.
Another consequence is accelerated enterprise adoption.
Businesses reluctant to build expensive GPU clusters can instead consume AI infrastructure as needed.
This reduces financial barriers.
Developers gain faster experimentation.
Enterprises shorten deployment cycles.
Model providers expand globally with lower capital requirements.
Competition may shift toward inference optimization.
Companies producing responses faster and cheaper will enjoy significant market advantages.
Infrastructure economics may become as important as model quality.
Cloud providers capable of maximizing GPU efficiency could outperform competitors despite having fewer physical resources.
NVIDIA also strengthens ecosystem lock-in.
Cloud partners adopting DSX AI factories naturally build around NVIDIA software, networking, and hardware.
Switching costs increase over time.
That reinforces
If execution succeeds, this strategy could become one of NVIDIA’s strongest competitive advantages over the next decade.
Rather than selling products, NVIDIA increasingly sells participation in an AI economy built around continuous computation.
Deep Analysis
Understanding
Linux remains the dominant operating system for nearly every large-scale AI factory due to its flexibility, networking capabilities, and GPU ecosystem.
Useful Linux commands for AI infrastructure administrators include:
Display installed NVIDIA GPUs nvidia-smi
Continuously monitor GPU utilization
watch -n 1 nvidia-smi
Check CUDA installation
nvcc –version
Display CPU information
lscpu
View available memory
free -h
Monitor running processes
htop
Check disk usage
df -h
Display PCI devices
lspci
Show network interfaces
ip addr
Test network latency
ping google.com
Check GPU processes
nvidia-smi pmon
View Docker containers
docker ps
List Kubernetes nodes
kubectl get nodes
Monitor Kubernetes pods
kubectl get pods -A
Display system uptime
uptime
Windows administrators frequently rely on:
Get-ComputerInfo Get-Process Get-Service Get-NetAdapter Get-Counter
macOS users working with AI development environments commonly use:
system_profiler SPHardwareDataType top vm_stat networksetup -listallhardwareports
Efficient monitoring across operating systems remains essential because AI factories depend on balanced GPU utilization, networking performance, storage throughput, and power efficiency rather than raw hardware counts alone.
✅ Fact: NVIDIA announced a strategy centered on DSX AI factories and revenue-sharing models to expand access to AI infrastructure. This aligns with the company’s broader push toward scalable AI cloud ecosystems.
✅ Fact: Sharon AI and Firmus have publicly been identified as early partners, with announced plans involving tens of thousands of NVIDIA GPUs and large-scale AI infrastructure projects.
✅ Fact: Demand for inference computing is growing rapidly across the AI industry. As generative AI moves into production, serving billions of AI-generated tokens has become one of the largest drivers of GPU infrastructure investment.
Prediction
(+1)
(-1) As more organizations depend on centralized AI infrastructure, concerns around energy consumption, supply chain constraints, GPU availability, and ecosystem lock-in could become increasingly significant challenges over the coming years.
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