The Real Challenge Limiting AI Models Today
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Modern AI systems are increasingly bottlenecked not by computational speed but by memory bandwidth — the rate at which data can be delivered to processors. As models grow to billions or trillions of parameters, moving data between memory and processors becomes the dominant performance constraint. The post explains different memory types used in AI (RAM, VRAM, HBM), distinguishes between memory capacity and bandwidth, and outlines how the bottleneck manifests differently during training versus inference. Researchers are exploring approaches like improved memory architectures, near-memory computing, model compression, and photonic interconnects to address this challenge.
