Text Generation Inference generally refers to the process of generating text from a trained language model given some input, and more specifically to frameworks that serve such models efficiently. In particular, Text Generation Inference (TGI) is the name of an open-source toolkit for deploying and serving large language models, released by Hugging Face​.TGI provides a high-performance inference server (written in Rust and Python) that is optimized for text generation workloads – it can handle features like model sharding, batch scheduling of requests, and streaming token output, enabling deployment of LLMs (like BLOOM, GPT-type models) in production with low latency. The goal is to maximize throughput and utilization when multiple generation requests are made. More broadly, when one discusses text generation inference, they may be talking about how a model like GPT-3 or GPT-4 is used at inference time: feeding a prompt and sampling or decoding the output text (using strategies like greedy, beam search, or nucleus sampling). Specialized inference engines (like Hugging Face’s TGI or OpenAI’s hosted inference) are important because large models are resource-intensive; they ensure that the model produces text responses efficiently and can scale to many users.