Performance Evaluation of Deep Learning Formats: A Comparative Study of ONNX and Pytorch for Inference Efficiency and Portability

The high computational cost of Large Language Models (LLMs) requires post-training optimization for efficient deployment. This paper presents a rigorous empirical comparison of inference performance between the native PyTorch framework and the optimized ONNX format for contemporary LLMs, including Gemma-7B, Phi-3-Medium, and Llama-3.1-8B, on NVIDIA GPU hardware. Using low-level profiling with NVIDIA's Nsight Compute suite, we found that ONNX conversion yields a substantial and statistically significant performance advantage. Our results show a latency reduction of up to 27.6% and an increase in computational throughput (GFLOP/s) of up to 48.3%. We conclude that adopting an ONNX-based strategy is not merely a portability choice but a critical optimization step for deploying high-performance, efficient LLMs in production environments.