Base Compute described an automated optimization pipeline that converts open-source model weights into hardware-specific inference implementations, using AI research agents to handle porting and performance tuning under strict accuracy gates. As a demonstration, the pipeline optimized NVIDIA’s Nemotron 3 Nano for Apple silicon, achieving 1.90 times the speed of llama.cpp and 1.43 times the speed of MLX on decode. The approach aims to make rapid, cost-effective model deployment across diverse hardware possible without manual per-combination engineering.
