How GPUs Actually Drive LLM Scaling: Insights from Stanford CS336 L5 2026

Modern artificial intelligence has shifted from a reliance on increasing CPU clock speeds to a horizontal scaling model utilizing GPUs and TPUs. This transition addresses the "memory wall," where computational power outpaces data transfer speeds, transforming software development into a hardware logistics challenge. Optimizing performance requires specialized techniques such as avoiding conditional branching, reducing numerical precision to 8-bit formats, and employing operator fusion to keep data within fast SRAM. Developers also utilize "recomputation"—discarding and recalculating intermediate variables—and data tiling to manage the physical constraints of silicon. The FlashAttention algorithm exemplifies this shift, enabling large language models to scale by using online softmax and aggressive tiling to bypass slow global memory bottlenecks. Ultimately, advancing AI necessitates a deep physical sympathy for hardware architecture rather than just abstract mathematical equations.