efficiency does not justify the sacrifice of accuracy.

Technological progress in machine learning has reached a point where massive models can now run on consumer-grade hardware through aggressive optimization. While this accessibility is touted as a democratization of power, it often obscures the fundamental trade-off between speed and the integrity of the output. The error here is the assumption that throughput is the ultimate measure of utility. If an optimization forces a compromise on the precision of the reasoning, it is not a gain in capability but a regression in quality. Speed is only a value when the product being accelerated remains entirely intact.