// Section 1 — The Model · 2 MIN READ

[✓] VERIFIED MANUAL ENTRY — This concept has been rewritten from primary sources and is legally cleared for production.

Inference

The execution phase where a trained model processes input tokens to generate output tokens.

Inference is the read-only phase of a model's life. Every time you ask a question or an agent requests a tool call, the client sends your prompt to a server (or local runner) which executes the parameters over the input tokens to generate the response.

Unlike training, which is a one-time capital expense, inference creates a recurring operational cost. Model providers charge per-token, separating input tokens (cheaper) and output tokens (more expensive because they must be computed sequentially).

Optimizing Inference Latency & Cost

  • Time to First Token (TTFT): The duration it takes to process the input prompt (prefill phase). This scales with input size, but can be optimized using Prompt Caching.
  • Inter-Token Latency (ITL): The duration between output tokens (generation phase). This is bound by GPU memory bandwidth since the model must read all parameters from VRAM for every single token generated.
  • Pruning: Keeping prompts short directly reduces both TTFT and total inference costs.

# AVOID

Do not assume that executing multiple identical prompts simultaneously will share generation costs.

  • Avoid: "Let's run 5 parallel agents with the same 100K token documentation block to speed up work."
  • Write: "We should verify if our provider supports prefix/prompt caching. Otherwise, running those 5 agents in parallel will cost 5x inference charges on the input prefill."

# USAGE

Developer A: "Our agent is taking 10 seconds to respond. Is it training?" Developer B: "No, it's running inference. The latency is high because the context window is stuffed with 50K tokens of old git logs, making the prefill phase slow."

// SEE_ALSO

ModelThe compiled, frozen parameters that calculate next-token probability distributions. A model is completely stateless and pure.
ParametersThe internal floating-point weights and biases of a neural network, optimized during training, that define the model's behavior.
TrainingThe computational process of optimizing a model's parameters by exposing it to datasets and adjusting weights via backpropagation.
TokenThe basic numerical chunk (character fragment or sub-word) that a model reads and writes, converted from text via a tokenizer.

// SOCRATIC_VALIDATION

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