LLM inference, the process of running a trained AI model to generate responses, is the primary cost for users, not model training. This process involves several steps, starting with converting user text into numerical tokens, which are then batched with other requests for efficiency. The inference process has two phases: prefill, where the model processes the entire prompt, and decoding, where it generates the output token by token. Understanding these steps can help users manage costs, as API providers often bill per token and may offer discounts for non-urgent tasks. AI
IMPACT Understanding LLM inference costs can help users optimize API usage and manage expenses for AI applications.
RANK_REASON Explainer article detailing the technical process and cost implications of LLM inference.
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