LLM Inference

Definitions

1. LLM Inference is the phase where a trained model processes new inputs to produce predictions or generations. It is commonly used for production APIs, batch jobs, and interactive assistants, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to latency, batching, and model loading behavior, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: LLM Inference is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.

   Example: "We added LLM Inference to the assistant stack so prompts stayed within the context window, outputs became more consistent, and the inference path stopped failing on long enterprise documents during peak traffic."

Related Terms

• Token
• Causal Language Model
• LLM Agent
• LLM Evaluation
• LLM Fine-Tuning
• LLM Routing
• LLM Safety
• Local LLM
• Long Context
• Multimodal Tokenizer
• OpenAI
• Output Token
• Prompt Caching
• Prompt Chaining
• Prompt Engineering Detail
• Prompt Injection Detail
• Prompt Template
• Prompt Tuning
• Stop Token
• Subword Tokenization
• System Prompt Detail
• Token Budget
• Token Limit
• Tokenization Detail
• Tokens Per Second Detail
• Zero-Shot Prompting