Choosing the right inference engine is critical for production AI systems. This article benchmarks TensorRT, ONNX Runtime, vLLM, and llama.cpp across latency, throughput, and memory usage. TensorRT excels on NVIDIA GPUs with optimized kernels, while ONNX Runtime offers cross-platform flexibility. vLLM is designed for large language models with efficient memory management, and llama.cpp targets edge devices with CPU-friendly performance. The benchmarks reveal that no single engine wins in all scenarios: TensorRT leads on GPU-bound tasks, vLLM shines for LLM serving, and llama.cpp is ideal for local inference. Developers should consider their hardware, model type, and latency requirements. This comparison provides a data-driven starting point for engine selection, though results may vary with specific models and hardware configurations.
A practical comparison of four major inference engines with benchmarks to guide deployment decisions.