Part 5/15:
The hypothesis: a massive increase in parameters (from hundreds of billions to trillions) may involve switching to sparse architectures. Sparse networks activate only parts of the model as needed, dramatically reducing resource requirements.
Implications:
To run a trillion-parameter dense model would require hundreds of terabytes of VRAM—costly and impractical.
Sparse models, with intelligent pruning and routing algorithms (like Google's Switch Transformer), could scale efficiently, handling larger sizes without insurmountable hardware demands.
Based on this, the consensus leans toward GPT-4 employing sparse architectures, possibly mimicking neurobiological structures, with selective activation of units akin to micro columns in the human brain.