pruning

An actionable guide to squeezing maximum performance from local AI models. It covers profiling to find bottlenecks; model slimming via quantization, pruning, and distillation; memory and compute tricks like KV-cache, batching, mixed precision, and offloading; device-specific accelerators; thermal/power tuning; and benchmarking to balance speed, accuracy, and privacy.