While graphics processing units (GPUs) once resided exclusively in the domains of graphic-intensive games and video streaming, GPUs are now equally associated with and machine learning (ML). Their ability to perform multiple, simultaneous computations that distribute tasks—significantly speeding up ML workload processing—makes GPUs ideal for powering artificial intelligence (AI) applications.
The single instruction multiple data (SIMD) stream architecture in a GPU enables data scientists to break down complex tasks into multiple small units. As such, enterprises pursuing AI and ML initiatives are now more likely to choose GPUs instead of central processing units (CPUs) to rapidly analyze large data sets in algorithmically complex and hardware-intensive machine learning workloads. This is especially true for large language models (LLMs) and the generative AI applications built on LLMs.
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