Conventionally, when evaluating deep neural networks, the Python interpreter must execute the code layer by layer to generate instructions that are then forwarded to a CPU or GPU. While this eager execution is fine for a single fast device, the single-threaded nature of the Python interpreter becomes a severe bottleneck when scaling to advanced multi-GPU servers (e.g., an 8-GPU instance). In such setups, Python struggles to generate instructions fast enough to keep all GPUs fully utilized. This bottleneck can be resolved by using hybrid programming to compile sequential models into optimized symbolic representations, thereby bypassing the Python interpreter during execution.