NVIDIA Leads the AI Chips Market: Who Are Its Main Competitors

NVIDIA’s chips have had high demand due to their exceptional processing capabilities and specialized design for AI workloads. The chips are used in robotics, healthcare, science, and many other fields. NVIDIA’s Hopper chip, in particular, the H100 model enables complex calculations and is the center of many modern data hubs. During the GTC Conference in March 2024, NVIDIA CEO Jensen Huang introduced the company’s latest GPU, Blackwell among other products. The chip is four times faster than the Hopper and requires less energy. Blackwell is the world’s most powerful GPU built specially for hyper-scale generative AI. 

Presenting the chip, Huang mentioned NVIDIA’s role in AI computing: 

NVIDIA has smashed records in recent years. In February 2024, its stock topped $277 billion in a day, surpassing the previous highest benchmark set by Meta. 

NVIDIA was founded in 1993, about 30 years ago. Its first product, a multimedia card called NV1, didn't see much demand. After an initial failure, NVIDIA switched its focus to designing GPUs for 3D graphics rendering. These chips gained popularity in video games, increasing the company’s revenue.

Back then, tech companies were mainly focused on producing CPUs (Central Processing Units). NVIDIA went against the flow and picked graphics rendering as the center of its strategy. The main difference between CPUs and GPUs is that CPUs are designed to handle all primary functions of the computer, while GPUs are designed to complement the work of CPUs by providing complex calculations and empowering certain tasks, such as quickly rendering high-resolution images and video. Companies have taken different approaches toward GPU use. Intel, a top company in the market, used GPUs to reduce the costs of computing tasks and offer consumers affordable solutions. NVIDIA, on the other hand, chose to maximize GPU’s performance for faster graphics rendering to take the visual quality of 3D games and digital content to a new level. Moreover, it was among the first companies to use GPUs in areas like deep learning, AI, and scientific research. 

What sets NVIDIA apart is its knack for creating in-demand solutions, predicting upcoming trends, and developing effective strategies. In 2017, the company announced the launch of graphics cards designed specifically for cryptocurrency mining, which soon became popular.

Who Are the Main Competitors of NVIDIA? 

NVIDIA’s dominant position can be changed as other companies are trying to catch up. Leading companies in the field include Google, Intel, Advanced Micro Devices (AMD), Qualcomm, Microsoft, and Alibaba. Other big names, like Google and Microsoft, are also taking steps to build their AI chips. 

In 2014, Google acquired the AI startup DeepMind to research and build AI systems. DeepMind is behind the development of AlphGo, an AI program to play the ancient board game of Go, which beat professional Lee Sedol. 

Continuing its AI journey, in 2017, Google introduced AI chips, called Tensor Processing Units, or TPUs. These chips are created specifically to handle AI tasks. They are designed in-house and trained on Google data. For the record, there are 6.3 million Google searches every minute, and this gives the company a huge advantage. In December 2023 Google introduced its large language model Gemini together with the latest TPU chip, Cloud TPU v5p, and AI Hypercomputer. TPU v5p is used to train large language models 2.8 times faster than the previous TPU v4 version. 

AMD is among the top companies producing GPUs and powerful processors. Its lineup of MI300 chips poses strong competition for NVIDIA. The latest model, the Instinct MI300X, was introduced in December 2023. Companies including Meta, OpenAI, and Microsoft were quick to adopt the chip. AMD claimed that the Instinct MI300X was twice as fast as NVIDIA’s H100 chip. Things got confusing as NVIDIA presented its own performance metrics, stating that when properly optimized, the H100 prevails. Anyway, NVIDIA’s brand-new Blackwell settles the argument, marking a new milestone that AMD may need to surpass. 

NVIDIA has been a key supplier of AI server chips for Microsoft. Microsoft used thousands of NVIDIA A100 GPUs to build the AI infrastructure for ChatGPT as a top investor and partner of OpenAI (the founder of ChatGPT). However, in 2023, Microsoft announced building its own custom AI chips: Microsoft’s Azure Maia AI chip and Cobalt 100. Both are expected to arrive this year. The chips are designed to power the services of Microsoft Azure, the company’s cloud computing platform. Although Microsoft initially will produce chips for the use of its data centers, the company may become NVIDIA’s close competitor in the long run. 

The UXL Foundation was launched by Linux Foundation as an evolution of the oneAPI initiative. oneAPI is an open-source programming model to support the development of  CPUs, GPUs, and other computational architectures. Describing UXL’s mission, Jim Zemlin, Executive Director at the Linux Foundation said: 

“The Unified Acceleration Foundation exemplifies the power of collaboration and the open-source approach. By uniting leading technology companies and fostering an open ecosystem of cross-platform development, we will unlock new possibilities in performance and productivity for data-centric solutions.

Among  UXL members are Google, Intel, Samsung, and Qualcomm. The companies have been achieving significant achievements in AI individually for years. One to mention is Intel’s winning an $8.5 billion grant from the US government as part of the CHIPS Act. This has been the biggest US government investment in AI chipmaking to date. Besides, the company can receive up to $11 billion in loans to build AI facilities. 

In March, UXL announced the building of a suite of solutions to power AI accelerator chips based on an open-source programming model. 

Beyond its members, the foundation will collaborate with Amazon, Microsoft’s Azure, and other companies. According to Reuters, the UXL engineers plan to identify the technical specifications of the project in the first half of this year. By then, the industry can only guess how the upcoming solutions will position UXL against NVIDIA. In fact, one of UXL’s goals is to help developers ‘migrate out from an Nvidia platform’, according to Qualcomm's head of AI and machine learning, Vinesh Sukumar.                                      

NVIDIA was the first mover in the AI race, and its dominance is clear. Over the years, the company has developed a comprehensive software ecosystem with high global demand. NVIDIA's success has set a benchmark in the industry. However, the company faces challenges as the number of solutions in the field is growing.