8 min read
By Jaber Jaber
By Jaber JaberHow to Break the Scaling Wall
When researchers plot model cross-entropy loss against compute on a log-log scale, the result is a near-straight line: loss falls predictably as compute, model size, or training tokens increase. That empirical regularity - the scaling law - lets teams forecast returns, but it also shows the limit: buying more GPUs gives diminishing marginal returns.
AI ScalingMachine LearningGPU OptimizationResearchPerformance
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