While focus is on massive supercomputers for training next-gen models, the real supply chain constraint will be 'inference' chips—the GPUs needed to run models for billions of users. As adoption goes mainstream, demand for everyday AI use will far outstrip the supply of available hardware.

NVIDIA is reportedly considering releasing its next-gen Rubin GPUs with less memory than announced due to supply constraints on high-bandwidth memory (HBM). This suggests fundamental hardware limitations, not just algorithms or data, may soon become the primary bottleneck slowing the pace of AI model growth.

AI workloads are limited by memory bandwidth, not capacity. While commodity DRAM offers more bits per wafer, its bandwidth is over an order of magnitude lower than specialized HBM. This speed difference would starve the GPU's compute cores, making the extra capacity useless and creating a massive performance bottleneck.

As AI models evolve to mirror the human brain, their memory requirements are skyrocketing, creating a 'RAMpocalypse.' The industry's focus will shift from being purely compute-centric to a dual focus on memory and compute, making high-bandwidth memory a critical and scarce resource.

While NVIDIA's GPUs have been the primary AI constraint, the bottleneck is now moving to other essential subsystems. Memory, networking interconnects, and power management are emerging as the next critical choke points, signaling a new wave of investment opportunities in the hardware stack beyond core compute.

While many focus on compute metrics like FLOPS, the primary bottleneck for large AI models is memory bandwidth—the speed of loading weights into the GPU. This single metric is a better indicator of real-world performance from one GPU generation to the next than raw compute power.

While training AI models is a compute-bound problem where more flops yield better results, inference (running the model) is memory-bound. Each token generation requires reading all model weights from memory, making memory bandwidth, not raw processing power, the primary performance bottleneck.

Previously, the biggest constraint in AI was compute for training next-gen models. Now, the critical bottleneck is providing enough compute for *inference*—the real-time processing of queries from a rapidly growing user base.

Unlike GPUs using slow, dense memory, Cerebras's wafer-sized chip leverages its vast surface area to accommodate faster, less-dense memory. This design sidesteps memory bottlenecks, achieving speeds up to 15 times faster than the fastest GPUs for AI tasks.