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However, there's a catch. As these models become more sophisticated, they also become more resource-intensive. Scaling up model parameters, which essentially means making them larger and more complex, requires enormous amounts of compute power. This translates to higher costs, more energy consumption, and greater latency, especially when deploying these models in real-time or edge environments.
The Importance of Test Time Compute
Test time compute refers to the computational effort used by a model when generating outputs, rather than during its training phase. As most large language models are designed to be incredibly powerful right out of the gate, they need to be big - really big. But this "bigger is better" approach comes with significant costs.