New Delhi: NVIDIA has introduced a new revenue-sharing and credit-support business model aimed at expanding access to large-scale AI computing infrastructure for startups, enterprises, AI model developers, research organizations, and regional cloud providers.
The initiative reflects the rapidly evolving AI landscape, where demand is shifting from training large language models to running production-scale AI inference that requires continuously operating, high-performance AI infrastructure.
By partnering with AI cloud providers, NVIDIA aims to make enterprise-grade accelerated computing more accessible while creating a recurring revenue stream tied to cloud usage.
New Business Model Expands AI Compute Access
Traditionally, building AI infrastructure has required significant capital investment in data centres, power, cooling systems, and GPU clusters, creating barriers for emerging AI companies.
NVIDIA’s new model seeks to address this challenge by enabling AI cloud providers to procure NVIDIA infrastructure under a commercial framework that combines hardware sales with revenue sharing.
Under the arrangement:
- AI cloud providers purchase NVIDIA AI infrastructure.
- They offer NVIDIA-powered cloud services to customers.
- NVIDIA receives standard hardware revenue alongside a share of cloud revenue generated from supported computing capacity.
The model is designed to accelerate the deployment of AI infrastructure while improving commercial flexibility for cloud providers and AI companies.
Supporting the Next Generation of AI Factories
The initiative is built around NVIDIA’s vision of AI factories—large-scale computing environments capable of continuously generating AI inference at production scale.
Unlike traditional data centres primarily designed for model training, AI factories support a broad range of workloads, including:
- AI model training
- Post-training optimization
- Fine-tuning foundation models
- High-volume AI inference
- Agentic AI applications
- Enterprise AI deployments
These facilities are designed to provide highly utilized, multi-tenant accelerated computing infrastructure capable of supporting AI applications around the clock.
Faster Access to AI Infrastructure
According to NVIDIA, the new commercial framework enables organizations to access large-scale computing resources without waiting for lengthy infrastructure development processes.
Customers can avoid delays associated with:
- Data centre site selection
- Power procurement
- Construction
- Hardware deployment
- Infrastructure commissioning
This significantly shortens the time required for startups and enterprises to move AI applications from development into production.
Sharon AI Deploys 40,000 NVIDIA GB300 GPUs
Among the first companies participating in the initiative is Sharon AI, which plans to deploy up to 40,000 NVIDIA Grace Blackwell GB300 GPUs.
The deployment is intended to support sovereign AI infrastructure while providing scalable computing capacity for customers building advanced AI applications.
According to Sharon AI, the collaboration represents an important milestone in expanding access to large-scale AI compute infrastructure.
Firmus Builds Large-Scale AI Factory in Indonesia
Another early participant is Firmus, which is developing a DSX AI Factory campus in Batam, Indonesia.
The facility is expected to scale to:
- 360 megawatts of computing capacity
- Support for up to 170,000 NVIDIA GPUs
The project aims to provide energy-efficient AI cloud infrastructure capable of supporting enterprises and AI-native companies across the Asia-Pacific region.
Growing Demand for AI Cloud Capacity
NVIDIA noted that AI-native companies increasingly require immediate access to high-performance computing infrastructure to support expanding workloads.
Organizations developing AI applications need scalable infrastructure for:
- Training foundation models
- Running production inference
- Building AI agents
- Serving enterprise customers
- Supporting developer platforms
As AI products transition from pilot projects to commercial deployment, demand for reliable GPU infrastructure continues to accelerate.
AI Infrastructure Becomes the New Competitive Advantage
The announcement highlights a broader shift within the AI industry.
Rather than competing solely through AI models, technology companies are increasingly investing in the infrastructure required to build, train, deploy, and operate AI systems at scale.
Industry analysts view AI compute, power availability, networking, and data centre capacity as becoming strategic assets in the next phase of AI adoption.
NVIDIA’s revenue-sharing approach positions the company not only as a supplier of AI hardware but also as a long-term infrastructure partner supporting AI cloud providers worldwide.
Outlook
NVIDIA’s new business model reflects the growing importance of scalable AI infrastructure as enterprises accelerate AI adoption.
By combining GPU hardware, cloud infrastructure, and recurring revenue partnerships, the company is helping expand access to accelerated computing while supporting the next generation of AI-native businesses.
As AI demand shifts toward production inference and always-on AI services, initiatives such as NVIDIA’s revenue-sharing model are expected to play an increasingly important role in shaping the global AI infrastructure ecosystem.
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Ruchi Kumar is the associate editor at Entrepreneur News Network and TVW News India, where she leads editorial strategy, brand storytelling, and startup ecosystem coverage. With a strong focus on innovation, business, and marketing insights, he curates impactful narratives that spotlight India’s evolving entrepreneurial landscape. She has written extensively on fintech, AI and emerging startups.