Innovation Machine learning: Accessing tomorrow’s tech
Machine learning and quantum computing technologies are impacting many industries, streamlining many processes and attracting long-term growth
Graphics processing units (GPUs), a type of technology that originated in the gaming world, have quickly evolved to help power the artificial intelligence (AI) revolution. These electronic circuits were developed to help create the highest quality visuals in modern gaming, but they also have many applications in AI.1
Parallel processing is one of the superpowers of GPUs that helps them excel at performing several complex tasks simultaneously. This is essential for rendering high-quality graphics and also for boosting AI workloads.
Indeed, strong demand for GPUs is being driven by technological advancements in areas like AI, virtual reality, cryptocurrency mining, autonomous driving, gaming platforms, and wearable technologies.2 The global GPU market is expected to hit about $274 billion by 2029, a compound annual growth rate of about 33% from 2024.3
Invesco QQQ ETF, which tracks the Nasdaq-100® Index, provides exposure to some of the leading companies in GPU development, including Nvidia, Advanced Micro Devices (AMD), and Intel.
Nvidia is the industry leader in GPUs, which helped the company get a head start in AI hardware and software. Nvidia shipped an estimated nearly 3.8 million data-center GPUs in 2023, a sharp increase from the previous year.4
Its GPUs are also highly sought after to handle the massive computational power required to train generative AI models like chatbots.
Nvidia's data center GPUs are widely employed in cloud computing and AI workloads. The company also has strategic partnerships with leading technology firms: the launch of the Blackwell architecture shows NVIDIA continues to innovate the GPU hardware and the speed of how quickly data is processed.
While AMD is one of the competitors behind Nvidia, the GPU market is growing so quickly that there are still plenty of opportunities. The company said it expects the market for AI accelerators in data centers could top $150 billion by 2027.5
AMD’s data center segment has been a bright spot due to rising demand for the company’s MI300 AI GPU, which has been fastest-ramping product in AMD history.6 Although it’s a distant second place to Nvidia, AMD has the ability to capitalize on GPU demand to accelerate AI workloads.
Chip giant Intel is a relatively new entry in the discrete GPU business with the introduction of its first-generation Arc Alchemist graphics cards 2022, which met mixed reviews. Intel also produces GPUs designed for data center use, particularly in accelerating AI computations.
Intel’s Arc graphics cards are popular with PC gamers and the company has plans to roll out its next generation of GPUs, codenamed Battlemage.7
As the demand for advanced computing capabilities and AI-driven technologies continues to rise, chip companies on the cutting edge of GPU production could continue to see opportunities for future growth. Invesco QQQ provides investors with a way to get exposure to this long-term theme and others powering the modern economy.
“CPU vs. GPU: Powerful options for your computing needs,” Intel.com.
“GPU Market Size, Share, and Trends 2024 to 2034,” Precedence Research, July 2023.
“GPU Market Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029),” Mordor Intelligence.
“Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study,” HPCwire, June 2024.
“AMD Gains After Chipmaker Tops Estimates, Makes AI Inroads,” Bloomberg, August 1, 2023.
“AMD’s MI300 AI GPU sales drive 80 percent growth in data center segment,” DatacenterDynamics.com, May 1, 2024.
“Intel Battlemage graphics cards: release date speculation, price, specs, and more,” DigitalTrends.com, May 20, 2024.
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