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Bits, FLOPS, and Watts: A Systems-Level Perspective of Scaling LLMs — Part 1 | by Asheesh Goja | Medium

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Scaling Large Language Models (LLMs) goes beyond simply increasing parameter counts. It emerges from a complex interplay of hardware, software, and algorithmic optimizations. These deep neural networks operate in high-dimensional vector spaces with complex loss landscapes and scaling power laws, where learning unfolds along intricate manifolds that often defy human intuition. Yet their existence is tethered to silicon and electricity — bound by thermodynamics, semiconductor physics, and the finite resources of our four-dimensional world. Ultimately, the mathematics of LLMs is bound by the very physics of silicon. (PDF version the paper) To understand their evolution, we must trace the journey of neural architectures from simple single-layer perceptrons to today’s trillion-parameter Transformers. At every stage, algorithmic breakthroughs and hardware advancements have propelled each other forward. As transistor scaling, memory bandwidth, and parallelism improved, each wave of innovation

Bits, FLOPS, and Watts: A Systems-Level Perspective of Scaling LLMs — Part 1 The Laws of Scaling Asheesh Goja 39 min read · Apr 5, 2025 -- Listen Share Introduction Scaling Large Language Models (LLMs) goes beyond simply increasing parameter counts. It emerges from a complex interplay of hardware, software, and algorithmic optimizations. These deep neural networks operate in high-dimensional vector spaces with complex loss landscapes and scaling power laws, where learning unfolds along intricate manifolds that often defy human intuition. Yet their existence is tethered to silicon and electrici

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