The key engineering optimizations in the DALL·E 1 paper include mixed-precision training and distributed optimization, which work together to address the immense challenges of training an extremely large-scale model (12 billion parameters). Mixed-precision training allows most model computations (such as forward and backward passes) to use FP16 precision for improved speed and memory savings, while maintaining an FP32 master copy of parameters to ensure numerical stability, combined with loss scaling to prevent gradient underflow. Distributed optimization builds on this by employing parameter sharding to split the model across multiple GPUs within the same machine to overcome per-GPU memory limits, and using gradient compression algorithms similar to PowerSGD to dramatically reduce inter-machine communication volume, overcoming network bandwidth bottlenecks.