Token ID

A token ID is the integer index of a token within its tokenizer vocabulary, the actual numerical representation that LLMs process internally. When text enters a model it is first tokenized into a sequence of token IDs, which are then converted into embedding vectors by the model's input embedding layer. Token IDs are the unit of attention computation, KV cache storage, sampling, and ultimately decoding back to text at output time. Token ID space is typically 0 to vocab_size-1, with low integers often reserved for special tokens (PAD=0, BOS=1, EOS=2 in many conventions) and higher integers covering the learned subword pieces. AI governance teams encounter token IDs mainly in low-level debugging of embedding pipelines, custom inference serving, or model interpretability work, since most application-level APIs deal in text or token strings rather than IDs. Token IDs are also the unit at which models like vLLM, TensorRT-LLM, and Llama.cpp internally manage memory, KV cache, and continuous batching for high-throughput serving.

Token ID accounting in Centralpoint: Centralpoint sits above low-level token ID processing and meters at the token level across whatever inference stack you operate. The model-agnostic platform supports OpenAI, Anthropic, Gemini, Llama, and embedded models, keeps prompts on-premise, and deploys chatbots through one line of JavaScript with audit-ready governance.


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