Jim Cramer Just Identified an AI Infrastructure Supplier Yet to Fully Price in April’s Compute Demand Surge

April was the month investors began to grasp just how important CPUs had become. Up until now, large language model (LLM) training was all about who could amass the most amount of GPUs to train their models. Leading up to Intel’s (INTC) recent earnings report, investors had already stopped asking how many GPUs AI needed. Instead, the focus shifted to how many CPUs were needed for each GPU to deliver optimal inference performance. Intel earnings recognized this shift, with CEO Lip-Bu Tan pointing out the obvious on the earnings call:

“CPU is very important when you move from training to inference. The inference side, I think in terms of orchestration, control plane and also managing all the different agent with data, CPU is much more efficient. I think the ratio of CPU to GPU used to be 1 and 8, and now it’s 1:4 and I think towards parity or even better.”

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