CUDA (Compute Unified Device Architecture)

The software platform that gave NVIDIA a near-monopoly on AI computing — and why every AI investment decision your organisation makes is influenced by this technology. NVIDIA's market capitalisation surpassed $3.5 trillion in 2025, making it one of the most valuable companies on Earth. The foundation of this dominance is CUDA — a software platform that enables developers to harness GPU parallel processing for AI and scientific computing. Over 90% of AI model training runs on NVIDIA GPUs using CUDA, and the platform has over 5.5 million registered developers worldwide as of June 2026. For business leaders, CUDA is the reason NVIDIA commands premium pricing for its chips and why alternatives have struggled to gain traction. Understanding CUDA is essential for understanding the economics and strategic dynamics of AI infrastructure.

What It Is

CUDA is a parallel computing platform and programming model created by NVIDIA in 2007. It allows software developers to use NVIDIA GPUs for general-purpose processing — not just graphics rendering. Before CUDA, GPUs were specialised hardware used almost exclusively for video games and visual effects. CUDA transformed GPUs into general-purpose parallel processors capable of accelerating any computation that could be broken into thousands of simultaneous operations. The business analogy is powerful: imagine you discovered that a factory originally built to manufacture one specific product could, with the right instructions, manufacture thousands of different products simultaneously at significant speed. CUDA provided those instructions. It gave developers a way to write code that directs thousands of GPU cores to work on problems in parallel — from training neural networks to simulating molecular interactions to processing financial risk models. The critical insight is that CUDA is not just hardware — it is an ecosystem. Over 18 years, NVIDIA has built an extensive library of CUDA-compatible tools, frameworks, and optimised algorithms. PyTorch and TensorFlow, the two dominant AI frameworks, are deeply optimised for CUDA. This means that virtually every AI model developed today is designed to run on CUDA-compatible NVIDIA GPUs. This software ecosystem creates a powerful moat that competitors like AMD (ROCm) and Intel (oneAPI) have struggled to penetrate despite offering competitive hardware.

Why It Matters for Business

CUDA's dominance directly affects your AI infrastructure costs. NVIDIA's AI accelerators — the H100, H200, and B200 series — sell for $25,000 to $45,000 per unit, commanding 70–80% gross margins that would be impossible without CUDA's software lock-in. The total cost of ownership for an AI training cluster, including networking, cooling, and power, can exceed $100 million for large deployments. CUDA compatibility is the primary reason organisations pay this premium rather than switching to lower-cost alternatives. The lock-in effect extends beyond hardware. Engineering teams trained on CUDA represent a significant investment — there are over 5.5 million CUDA developers globally as of June 2026, and their expertise does not transfer easily to competing platforms. For CTOs, this means that a decision to build on CUDA today is a long-term strategic commitment with implications for hiring, vendor relationships, and infrastructure flexibility. However, the landscape is shifting. AMD has invested over $1 billion in developing ROCm, its CUDA alternative, and has secured partnerships with Meta, Microsoft, and the French government for large-scale AI deployments using AMD GPUs. OpenAI has reportedly invested in developing its own AI chip, and Google's Tensor Processing Units (TPUs) offer a CUDA-free path for AI workloads. These alternatives are viable for inference workloads today and increasingly competitive for training.

Enterprise Use Cases

OpenAI trained GPT-4 and its successor models on a massive cluster of NVIDIA H100 GPUs using CUDA, reportedly spending over $100 million on compute for GPT-4 training alone. The decision to use CUDA was not really a decision at all — the entire AI research ecosystem is built on CUDA, making it the only practical choice for frontier model training. This dependency is precisely why OpenAI, Google, and others are investing in alternatives. Tesla processes data from its fleet of over 5 million vehicles using NVIDIA GPUs and CUDA to train its autonomous driving neural networks. Tesla's AI infrastructure, comprising tens of thousands of NVIDIA GPUs, processes petabytes of driving data to improve its Full Self-Driving capability. Tesla also developed its own custom AI chip (Dojo) to reduce its CUDA dependency, illustrating both the power of CUDA and the strategic desire to diversify. Financial services firm Citadel uses CUDA-powered NVIDIA GPUs to run real-time risk models and algorithmic trading strategies. The firm reported that CUDA-accelerated Monte Carlo simulations run 200 times faster than CPU-only implementations, enabling risk analyses that were previously impossible within intraday timeframes. This computational advantage translates directly into trading performance and risk management capability.

Vendor Landscape

NVIDIA is the sole provider of CUDA-compatible hardware. Its product line spans from data centre AI accelerators (H100 at approximately $25,000–30,000, H200 at $35,000–40,000, and the B200 Blackwell at $40,000–45,000) to professional workstations (RTX 6000 Ada at approximately $7,000). Competing platforms include AMD ROCm (compatible with AMD Instinct MI300X accelerators priced approximately 30–40% below equivalent NVIDIA hardware), Intel oneAPI (for Intel Gaudi accelerators), and Google TPUs (available exclusively through Google Cloud). The open-source Triton project, backed by OpenAI, aims to provide a vendor-neutral GPU programming layer that could eventually reduce CUDA dependency. Cloud providers offer NVIDIA GPU instances on demand: AWS P5 instances (8x H100 GPUs) cost approximately $32 per hour as of June 2026, reflecting the ongoing demand for CUDA-compatible solutions in cloud environments.