ChatGPT's explosion in popularity has stirred up some fears that another GPUshortage is on the horizon, but gamers don't need to worry.
ChatGPT is exploding, and the backbone of its AI model relies on Nvidia graphics cards. One analyst said around 10,000 Nvidia GPUs were used to train ChatGPT, and as the service continues to expand, so does the need for GPUs. Anyone who lived through the rise of crypto in 2021 can smell a GPU shortage on the horizon.
Related CUDA is Nvidia’s Application Programming Interface used in everything from its most expensive data center GPUs to its cheapest gaming GPUs. CUDA acceleration is supported in machine learning libraries like TensorFlow, vastly speeding training and inference. CUDA is the driving force behind AMD being so far behind in AI compared to Nvidia.
Again, Nvidia’s gaming GPUs like the RTX 4080 have Tensor cores . However, for all of the specs Nvidia cards have to accelerate AI models, none of them are as important as memory. And Nvidia’s gaming GPUs don’t have a lot of memory. You can get by with less, but you still need a lot of memory. Heaton recommends beginners have no less than 12GB, while a typical machine learning engineer will have one or two 48GB professional Nvidia GPUs. According to Heaton, “most workloads will fall more in the single A100 to eight A100 range.” Nvidia’s A100 GPU has 40GB of memory.
Different needs, different dies Nvidia’s gaming GPUs generally aren’t suitable for AI due to how little video memory they have compared to enterprise-grade hardware, but there’s a separate issue here as well. Nvidia’s workstation GPUs don’t usually share a GPU die with its gaming cards.
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