Nvidia (NVDA) dominates the global AI chip market with an estimated 70-90% share, driven by its high-powered graphics processors. This demand helped the company reach a market cap of $1 trillion in June and saw its shares reach an all-time high of $549.91. But it’s not just the hardware that keeps Nvidia ahead; it’s also the company’s Cuda software, crucial for creating AI platforms.
Nvidia’s success in AI didn’t happen overnight; it has been working on AI products for years, helping to create new markets. However, threats to its dominance are emerging from rivals like Intel (INTC) and AMD (AMD), both launching data center accelerators to compete with Nvidia. Additionally, hyperscalers like Microsoft (MSFT), Google (GOOG, GOOGL), Amazon (AMZN), and Meta (META) are developing their own ASICs, posing a challenge to Nvidia’s GPUs.
While AI graphics accelerators like those from Nvidia, AMD, and Intel have been versatile in handling various AI tasks, ASICs are built for specific AI needs, making them more efficient than GPUs. However, Nvidia still remains ahead in terms of technology, as it has a long-term research pipeline to drive future GPU leadership.
In the AI chip space, there are two primary uses: training and inferencing. Training models and putting those models into practice are crucial. Over time, inferencing is expected to become the primary use case for AI chips as more companies seek to take advantage of different AI models. Despite potential challenges to its market share, Nvidia’s revenue is expected to continue growing as the AI industry expands.