Training and inference efficiency

The computational cost of training and serving large language models grows faster than hardware improvements can offset. Scaling laws suggest diminishing returns without architectural innovation. Mixture-of-experts, state-space models, linear attention variants, and speculative decoding offer paths to efficiency, but each introduces new trade-offs in quality, memory, or engineering complexity. Achieving compute-optimal scaling while maintaining capability across diverse tasks is critical for sustainable AI development.