NVIDIA NIM vs Triton vs vLLM: Choosing an Enterprise Inference Runtime Without Benchmark Theater

This article provides a detailed analysis of enterprise inference runtimes for large language models, specifically NVIDIA NIM, NVIDIA Triton Inference Server, vLLM, and TensorRT-LLM. It argues that choosing based solely on benchmark performance is misleading and emphasizes operational factors like latency, mixed workloads, model updates, autoscaling, security, and support. The article clarifies that NIM packages vLLM, Triton is a general-purpose server, vLLM is an LLM-focused engine, and TensorRT-LLM is an optimized library. It offers guidance on when to use each: NIM for validated NVIDIA-supported services, Triton for mixed model estates, vLLM for flexibility and open-source APIs, and TensorRT-LLM for maximum performance on NVIDIA hardware. The key takeaway is to standardize on a portfolio of runtime profiles rather than a single binary.