In just a few years, NVIDIA went from a company best known for gaming graphics cards to the central supplier of the AI boom. Competitors make powerful chips too, yet NVIDIA’s share of AI accelerators has remained dominant. Understanding why requires looking beyond the hardware.

An early bet on general-purpose GPUs

In 2006–2007 NVIDIA introduced CUDA, a platform that let programmers use GPUs for general computation, not just graphics. For years, this looked like a niche investment serving scientists and researchers. Then, in 2012, the AlexNet neural network — trained on NVIDIA GPUs using CUDA — won the ImageNet competition by a wide margin. Deep learning researchers adopted NVIDIA hardware and software en masse, and the company kept investing in AI-specific features.

The software moat

CUDA grew into a vast ecosystem:

  • Libraries such as cuDNN for neural networks and cuBLAS for linear algebra, tuned for each new chip generation.
  • Framework integration: PyTorch, TensorFlow and JAX are deeply optimized for NVIDIA GPUs.
  • Inference tools such as TensorRT for running models efficiently.
  • Developer habits: a generation of AI engineers learned on CUDA, and most research code assumes it.

Switching to another chip means porting and re-optimizing software, retraining staff and accepting risk. That friction is the “moat”.

Networking and full systems

Training frontier models requires thousands of GPUs to work as one. NVIDIA acquired Mellanox, a maker of high-performance networking (including InfiniBand), completing the deal in 2020. It also developed NVLink to connect GPUs directly at high speed. Today NVIDIA sells not just chips but complete systems — servers, rack-scale designs and networking — tuned to work together.

Supply chain position

NVIDIA designs chips but relies on manufacturing partners, especially TSMC, and on high-bandwidth memory suppliers. Securing capacity at scale during periods of shortage became a competitive advantage in itself.

The challengers

  • AMD offers Instinct accelerators and the open ROCm software stack, and has won major customers.
  • Custom chips from Google (TPU), Amazon (Trainium, Inferentia), Microsoft (Maia) and Meta (MTIA) target their owners’ workloads.
  • Startups pursue new architectures for faster or cheaper inference.
  • Software abstraction: compilers and frameworks that target multiple hardware backends could weaken CUDA lock-in over time.

Limits of the moat

The largest AI buyers have strong incentives to reduce dependence on a single supplier, and inference — a growing share of AI computing — is often more amenable to specialized chips than training. Export controls also shape which products NVIDIA can sell in certain markets. Dominance in AI hardware is strong, but not guaranteed forever.

What could change the picture

Shifts that could erode the lead include widely adopted open software stacks that run equally well on other chips, hyperscalers moving more internal workloads to custom silicon, and inference workloads where specialized chips offer better cost per token.

Related: GPU vs TPU vs NPU.

What to watch next

  • Inference economics. As serving models becomes the dominant AI workload, specialized inference chips could win share where flexibility matters less.
  • Software portability. Compilers and frameworks that run the same model code efficiently on several hardware types would weaken lock-in.
  • Custom silicon at hyperscalers. The share of internal workloads Google, Amazon, Microsoft and Meta move to their own chips is a key indicator.
  • Supply chain capacity. Advanced packaging and high-bandwidth memory supply have repeatedly limited how many accelerators can be built.
  • Export controls. Trade restrictions influence which products can be sold where and encourage domestic alternatives in affected markets.

Key takeaways

  • NVIDIA launched CUDA in 2006–2007, making GPUs programmable for general computing.
  • AlexNet’s 2012 success on NVIDIA GPUs tied deep learning to its platform.
  • Libraries, framework optimizations and developer habits form a strong software moat.
  • Networking (Mellanox, NVLink) and full-system designs extend its advantage.
  • AMD, custom cloud chips and startups compete, especially for inference.

Frequently asked questions

Can AI models run on non-NVIDIA chips?

Yes. Major frameworks support AMD GPUs, Google TPUs and other accelerators, though performance tuning and tooling maturity vary.

What is CUDA?

NVIDIA’s parallel computing platform and programming model, together with the libraries and tools that let software use NVIDIA GPUs for general computation, including AI.

Why don’t big tech companies just use their own chips?

They increasingly do for some workloads, but GPUs remain the most flexible option and the standard for much research and many customers.