“Every semiconductor company in the world sucks at software except for NVIDIA.”
— Dylan Patel
Software Moat:
CUDA & Beyond: CUDA is just one layer of NVIDIA’s software stack. Beneath that are dozens of networking and management layers.
For training large models, developers rely heavily on NVIDIA’s robust software tools.
Hardware and Speed of Execution:
Annual Cadence: NVIDIA aims for major GPU/system releases every single year, pushing TCO/performance gains of 5× or more.
Rapid Design to Deployment: They partner closely with TSMC and the entire supply chain to get new architectures out first.
Networking (via Mellanox Acquisition):
System-Level Architecture: Today’s workloads require coherently linking potentially thousands of GPUs into a single HPC cluster.
Rack-Scale Complexity: A modern “purchased unit” is often an entire rack of GPUs, optics, cables, and advanced cooling.
Vulnerability?
Competitors like AMD and custom ASIC efforts at Google/Amazon might chip away at certain slices of the market (especially inference), but NVIDIA’s unmatched “full-stack approach” (software + silicon + systems) remains uniquely strong—particularly for training.