In Depth

Inference latency and throughput are critical production engineering concerns. Optimization techniques include quantization (reducing weight precision), pruning, speculative decoding, batching, and hardware-specific compilation. As models grow larger, inference costs increasingly dominate total AI infrastructure spend, driving demand for specialized inference chips and edge deployment.