vLLM vs TensorRT-LLM: Key differences, performance, and how to run them

Large language models (LLMs) have evolved from research concepts to practical applications in various domains, but efficiently serving them remains a challenge, necessitating high-performance inference backends like vLLM and TensorRT-LLM. Both systems aim to optimize GPU usage for LLMs, yet they employ distinct methodologies: vLLM uses PagedAttention and asynchronous GPU scheduling to enhance throughput and reduce latency, while TensorRT-LLM leverages CUDA graph optimizations and Tensor Core acceleration for peak performance on NVIDIA GPUs. vLLM is open-source and integrates easily with the Hugging Face ecosystem, making it flexible and suitable for diverse pipelines, whereas TensorRT-LLM is tightly integrated with NVIDIA's enterprise stack, offering advanced optimizations but requiring more complex setup. The choice between them depends on specific use cases, with vLLM being ideal for fast integration and flexibility, and TensorRT-LLM excelling in environments where maximum NVIDIA GPU efficiency is paramount. Northflank, a full-stack AI cloud platform, facilitates the deployment and scaling of both inference engines, allowing users to leverage the strengths of each system as needed.