![]() ARM processors are even more common, with over 250 billion chips shipped through the third quarter of 2022.ĪI developers have largely ignored this pool of untapped potential, assuming that the CPU’s relative lack of parallel processing would be a poor fit for deep learning, which typically relies on numerous matrix multiplications performed in parallel. A recent report from PC component market research firm Mercury Research found that 374 million x86 processors were shipped in 2022 alone. ![]() The ubiquity of CPUs provides a workaround to the GPU’s dominance. “For all these reasons, we need an alternative, and Intel CPUs work very well in many inference scenarios, if you care to do your homework and use the appropriate tools.” “It’s near impossible to get an A100 on AWS or Azure. Yet Simon believes that “monopolies are never a good thing.” The GPU’s dominance may exacerbate supply-chain issues and lead to higher costs, a possibility underscored by Nvidia’s blowout Q1 2023 financial results, in which earnings rose 28 percent on the back of demand for AI. Nvidia’s A100 is a powerful tool for AI, but high demand has made the hardware tough to obtain. Hugging Face, as a central hub for the AI community that (among other things) provides an open-source Transformers library compatible with TensorFlow and PyTorch, has played a role in CUDA’s growth, too. It was well established by the middle of the 2010s, providing TensorFlow and PyTorch a clear route to tap the power of Nvidia hardware. “GPUs were then quickly integrated in open-source frameworks like TensorFlow and PyTorch, making them easy to use without having to write complex low-level CUDA code.”Ĭompute Unified Device Architecture (CUDA) is an application programming interface (API) that Nvidia introduced in 2007 as part of its plan to challenge the dominance of CPUs. “Unlocking the massively parallel architecture of GPUs to train deep neural networks is one of the key factors that made deep learning possible,” says Simon. Several specific events helped GPU hardware outmaneuver both CPUs and, in many cases, dedicated AI accelerators. GPU usage in AI development is so ubiquitous that it’s hard to imagine another outcome, but it wasn’t inevitable. ![]() The demo offers a chat interface like OpenAI’s ChatGPT and responds to queries at blazing speeds that (from personal experience) leave ChatGPT eating dust. Julien Simon, the chief evangelist of AI company Hugging Face, recently demonstrated the CPU’s untapped potential with Intel’s Q8-Chat, a large language model (LLM) capable of running on a single Intel Xeon processor with 32 cores. That’s the conclusion reached by a small but increasingly vocal group of AI researchers. It’s time to give the humble CPU another crack at AI.
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