Large Language Model (LLM)
AI & MLLarge Language Model (LLM) is a neural network trained on large text corpora to predict and generate language, enabling tasks like chat, summarization, and code assistance. In web hosting, LLMs appear as integrated support bots, content tools, and developer copilots, often accessed via APIs or self-hosted inference servers. Their requirements affect CPU/GPU needs, memory, latency, and data handling choices.
How It Works
An LLM is typically built with a transformer architecture that learns statistical patterns in language by training on massive datasets. During inference, it takes a prompt (input text) and predicts the next tokens step by step, producing responses that can follow instructions, answer questions, or generate structured output like JSON. Behavior is shaped by model size, training data, and alignment methods such as instruction tuning and reinforcement learning from human feedback.
In practical deployments, LLMs are used in two main ways: via a hosted API or by running the model on your own infrastructure. API use offloads compute and scaling to a third party, while self-hosting requires an inference stack (often containerized with Docker) and careful resource planning. Key operational factors include context length (how much text the model can consider), token throughput, concurrency, caching, and guardrails such as content filtering and prompt injection defenses.
Why It Matters for Web Hosting
LLM features can change what you need from a hosting plan. If you only call an external LLM API, your hosting priorities are outbound connectivity, request latency, and secure secret management for API keys. If you self-host an LLM, you must evaluate whether the provider offers GPU instances, sufficient RAM and fast storage, and network capacity for concurrent users. Data residency and privacy also matter when prompts include customer data, logs, or proprietary content.
Common Use Cases
- Customer support chatbots embedded in websites or control panels
- Content drafting, rewriting, and SEO assistance for CMS workflows
- Developer copilots for code generation, refactoring, and documentation
- Search and knowledge-base Q&A using retrieval-augmented generation (RAG)
- Log analysis and incident triage summaries for operations teams
- Form filling, classification, and automated email responses in web apps
Large Language Model (LLM) vs Machine Learning Model
An LLM is a specific type of machine learning model focused on language generation and understanding, usually large and resource-intensive. Many other ML models are smaller and task-specific (for example, image classifiers or anomaly detectors) and can run efficiently on CPUs with modest memory. For hosting decisions, LLMs more often drive requirements for GPUs, higher RAM, and careful latency planning, while traditional ML workloads may prioritize batch processing, storage, or simpler inference endpoints.