LLMs are deployed in real-world applications using APIs, on-premises infrastructure, or cloud-based solutions. For smaller-scale applications, APIs like OpenAI’s GPT offer a convenient way to access LLM capabilities without handling infrastructure. Developers integrate these APIs into their software via SDKs or RESTful endpoints.
For larger-scale or domain-specific deployments, organizations often fine-tune LLMs and host them in private environments. Deployment tools like Docker and Kubernetes enable scalable and reliable hosting, while model-serving frameworks such as TensorFlow Serving or Hugging Face Inference Toolkit streamline inference. Cloud platforms like AWS, Azure, and Google Cloud provide managed services for hosting and scaling LLMs.
Real-world applications include chatbots, automated content creation, sentiment analysis, and recommendation systems. These deployments often incorporate additional layers, such as monitoring and logging, to ensure performance and reliability. Security measures, such as access control and encryption, are critical for protecting sensitive data during deployment.