Introduction
In contemporary AI research and development, foundation models, typically large neural networks pre-trained on massive, diverse datasets and capable of rapid adaptation to downstream tasks, are central to breakthroughs in natural language processing, vision, multimodal systems and physical reasoning. While the conceptual and algorithmic foundations of such models predate NVIDIA’s involvement, the realisation and wide-scale deployment of these systems have depended heavily on hardware and software innovations that facilitate efficient training and inference at scale.
This paper examines NVIDIA’s historical trajectory and ongoing work in relation to foundation models, arguing that NVIDIA’s leadership in GPU design, AI software ecosystems and compute infrastructure has been pivotal in enabling the scaling regimes that underlie large AI models today. It situates NVIDIA’s contributions not merely as technological artefacts, but as socio-technical forces shaping the contours of contemporary AI research, industry practices and global competitive dynamics.
Origins and Early Development
Established in 1993 by Jensen Huang, Chris Malachowsky and Curtis Priem, NVIDIA began as a specialist in graphics processing units (GPUs), with its early innovations focused on gaming and visualisation markets. Its GeForce series of GPUs paved the way for high-performance parallel computation, which would become central to AI computing decades later. By the 2010s, NVIDIA’s GPU technology had become attractive to researchers seeking hardware capable of parallelising neural network training workloads economically, long before AI became a mainstream industrial focus.
The transformation of NVIDIA’s relevance to AI can be traced to the early 2010s when deep learning researchers discovered that GPUs, originally designed for graphics throughput, were remarkably effective at parallelising the matrix and tensor operations characteristic of neural network training. Researchers such as Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton trained AlexNet, a deep convolutional neural network that dramatically outperformed previous approaches on the ImageNet benchmark, using a pair of NVIDIA GTX 580 graphics cards in 2012. This experiment not only underscored the viability of GPU-accelerated deep learning but also marked a pivotal moment that helped shift NVIDIA’s strategic orientation toward AI.
Responding to this emergent trend, NVIDIA developed CUDA (Compute Unified Device Architecture) in the mid-2000s, a programming platform and API that enabled general-purpose GPU computing long before deep learning became a dominant research paradigm. By abstracting GPU parallelism for broader scientific computing, CUDA laid the foundation for deep learning frameworks to exploit GPU acceleration across research labs and industry.
Architectural Innovations for AI
NVIDIA’s architectural innovations have profoundly shaped the hardware landscape for AI:
• Tensor Cores and Volta: In 2017, NVIDIA introduced the Volta microarchitecture, which added specialised Tensor Cores designed explicitly for accelerating deep learning operations. Tensor Cores enhanced matrix-multiply throughput, improving training and inference performance and signalling NVIDIA’s strategic pivot toward AI-driven computing.
• Blackwell and Next-Generation AI: The Blackwell architecture, announced in 2024, was designed explicitly as a generative AI-era processor, with improvements tailored for transformer and large model workloads. Blackwell underpins a range of data-centre and consumer GPUs, including the GeForce RTX 50 Series, which supports new compute formats such as FP4 to accelerate inference and training tasks on consumer hardware.
• Future Platforms: CES 2026 saw NVIDIA reveal the Vera Rubin AI computing platform, integrating CPU, GPU, networking and security elements to create a rack-scale AI supercomputer capable of training complex mixture-of-experts models with significantly reduced resource usage.
These architectural innovations reflect NVIDIA’s strategy to optimise hardware not only for raw performance but for scaling efficiency, a central concern for foundation model training and deployment where computational cost is often a primary bottleneck.
Integrated Systems and the DGX Line
Beyond discrete GPUs, NVIDIA developed turnkey systems such as the DGX series, integrated hardware and software solutions that serve as reference platforms for AI training clusters. The DGX line has evolved through successive GPU generations, offering high-speed NVLink connectivity and dense GPU configurations that cluster together seamlessly, enabling research labs and enterprises to deploy large models more efficiently.
Software Ecosystems and Developer Enablement
NVIDIA’s role in shaping the software ecosystem for AI is as significant as its hardware innovations. Contributions to deep learning frameworks, both through low-level libraries and integration with high-level tools, have streamlined GPU-accelerated training:
• cuDNN and CUDA Libraries: NVIDIA’s cuDNN library, released in 2014, provided highly optimised primitives for deep neural networks that integrated with major frameworks such as TensorFlow and PyTorch. Because cuDNN maximises GPU utilisation for neural network layers, it became a critical factor in accelerating model training.
• Framework Support: NVIDIA consistently contributed to open-source deep learning frameworks (e.g. support for Tensor Cores, mixed-precision training and performance optimisations in frameworks such as MXNet, Caffe and PyTorch). By doing so, it helped bridge the gap between hardware capability and researcher usability.
• NeMo and Nemotron Models: More recently, NVIDIA’s NeMo suite provides an end-to-end platform for building, training and deploying foundation models, including Nemotron reasoning-oriented models that aim to balance inference efficiency with accuracy on large-language tasks. These models, along with datasets and training recipes shared on public platforms like Hugging Face, illustrate NVIDIA’s dual role as both hardware provider and model ecosystem contributor.
Collectively, these software contributions reduce friction for AI developers and integrate GPU acceleration deeply within common research and development workflows.
NVIDIA and Foundation Model Training
Although NVIDIA does not historically derive its reputation from developing foundational generative models in isolation; those breakthroughs are often credited to specialist labs at OpenAI, Google, Meta, etc., its hardware has been indispensable to training and scaling these systems. Research and industry widely deploy NVIDIA GPUs to train large transformer-based architectures because of their unparalleled performance in matrix-intensive operations. Indeed, NVIDIA controlled more than 80 % of the market for GPUs used in training and deploying AI models as of 2025, making it foundational to the very production of such models.
NVIDIA’s Own Foundation Model Initiatives
The company has also begun to publish and support its own foundation model platforms:
• AI Foundation Models for RTX AI PCs: In early 2025, NVIDIA launched foundation models optimised to run locally on consumer RTX AI PCs via NIM micro-services and AI Blueprints, enabling developers to build workflows and agents on desktop hardware. These models leverage the RTX 50 Series GPUs and broaden access to generative AI capabilities beyond cloud infrastructure.
• Cosmos World Foundation Models and Physical AI: NVIDIA announced its Cosmos world foundation model platform, designed for physical artificial intelligence tasks such as robotics and autonomous vehicles. The Cosmos platform combines generative world models, video tokenisation and synthetic data engines to support research and development across embodied AI domains.
• Isaac GR00T N1: In 2025, NVIDIA introduced Isaac GR00T N1, an open and fully customisable foundation model for humanoid robot reasoning, developed in collaboration with Google DeepMind and Disney Research. This model exemplifies NVIDIA’s push into physical AI foundation models, extending the concept of foundational AI beyond language and vision into embodied agency.
These initiatives demonstrate a strategic broadening from hardware enablement to model provisioning, where NVIDIA actively engages with generic AI systems tailored for specialised domains, particularly robotics and real-world environment interaction.
A Hybrid Role in the AI Ecosystem
NVIDIA’s foundation model engagement reflects a hybrid role in the AI ecosystem. On the one hand it supplies essential hardware and software infrastructure used by diverse model developers. On the other, it curates and optimises foundational models that run natively on its accelerated compute stacks, integrating model deployment with hardware performance. This dual stance differentiates NVIDIA from labs focused primarily on model research or consumer productisation, positioning it as an infrastructural gateway between model creation and real-world application.
Partnerships, Cloud Integration and National AI Capacity
To maximise the impact of its technologies, NVIDIA has cultivated deep partnerships across cloud providers and industrial players. Its GPUs form the backbone of AI infrastructure across Amazon Web Services, Microsoft Azure, Google Cloud and many specialised cloud brokers. These ecosystems offer GPU-accelerated instances optimised for large foundation model training and inference, enabling organisations to scale AI workloads without owning physical hardware. Complementary agreements, such as GPU allocations for national AI infrastructure projects (e.g., in the United Kingdom) reinforce NVIDIA’s centrality to global AI deployment strategies.
NVIDIA’s acquisitions and investments reflect a strategic emphasis on ensuring its compute infrastructure remains indispensable to AI workflows. For example, the acquisition of SchedMD, the steward of the open-source Slurmscheduler, underscores the importance of orchestration and workload management for large training jobs typical of foundation model research and deployment. Maintaining Slurm’s open-source status further signals a nuanced engagement with open ecosystem tools crucial to large-scale AI research.
Beyond commercial partnerships, collaborations with national governments (such as the partnership with the South Korean government) demonstrate NVIDIA’s role in shaping sovereign AI capacity. Supplies of hundreds of thousands of GPUs to national AI cloud centres highlight how compute infrastructure is a key vector in geopolitical competition for AI leadership.
Ethical, Political and Environmental Implications
NVIDIA’s predominance in GPU hardware for AI has ethical and political ramifications. The near-monopolistic share of AI compute resources raises questions about power concentration in global research ecosystems, potentially limiting diversity in research directions and amplifying dependency on a single supplier for both hardware and associated software stacks. Such centralisation may influence which research agendas are feasible and which are marginalised, raising questions about industrial governance in AI.
The energy intensity of training large foundation models draws increasing scrutiny. While NVIDIA’s hardware innovations often improve performance-per-watt, the sheer magnitude of compute deployed for training and inference continues to expand prompting research into sustainable co-design between hardware and software. Academic work underscores the importance of efficient hardware-software co-design to reduce environmental impact without compromising performance.
Access to cutting-edge hardware remains uneven globally, with developed economies and large corporations having disproportionate capacity to deploy and train the largest models. While cloud platforms mitigate some barriers, the cost and scarcity of high-end GPUs sustain inequities in research participation, with implications for whose values and priorities are embedded in foundation models.
Conclusion
NVIDIA’s historical trajectory from graphics chips to an AI infrastructure powerhouse exemplifies how foundational technologies shape the evolution of an entire scientific and industrial field. Through innovations in GPU architecture, the development of software ecosystems and evolving engagement with foundation model provisioning, NVIDIA has become essential to the scaling, accessibility and performance of large AI systems. Its hardware and software form the substrate upon which many contemporary foundation models are trained, deployed and optimised.
Moreover, NVIDIA’s hybrid role, as both infrastructure provider and active participant in model ecosystems, positions it uniquely in the AI landscape, bridging the technical and commercial domains that underpin modern AI. At the same time, its dominance raises complex socio-technical questions about agency, equity and power in global AI research. Understanding NVIDIA’s contributions offers insight not only into the technical evolution of AI but also into the institutional and infrastructural dynamics that shape the field’s future.