Large Language Models (LLMs) serve as the foundation for AI applications like chatbots and virtual assistants, but deploying them in production often presents challenges related to performance, such as latency and memory constraints. To address these issues, LLM inference engines and servers optimize memory usage and improve throughput, ensuring efficient handling of requests. Notable solutions include vLLM, which uses the PagedAttention algorithm to enhance throughput, and TensorRT-LLM from NVIDIA, which optimizes performance but is limited to NVIDIA hardware. Hugging Face's Text Generation Inference employs tensor parallelism for better performance, while RayLLM with RayServe offers scalable deployment options and supports continuous batching. Triton Inference Server, another NVIDIA product, accelerates LLM deployment with dynamic batching and efficient caching but also requires NVIDIA GPUs. Choosing the right inference engine or server depends on specific use cases, model sizes, and latency needs, with each solution offering unique optimizations and features tailored to different deployment scenarios.