What is inference?
Inference is the use of a trained model to produce an output for an input it has not seen before. Training happens once, or periodically, and produces the model weights. Inference happens every time the model is asked a question, scores a record, or generates text, and it continues for as long as the model is in service. For a classifier, inference returns a class and a confidence score. For a large language model, inference generates the response one token at a time.
The computational profile differs from training. Training is a long, heavy job on a large dataset. Inference is a stream of smaller jobs whose cost depends on model size, input length, and how many requests arrive at once. Small classic models run comfortably on a CPU. Large language models need a GPU with enough memory to hold the weights, and an inference server that batches requests and manages the KV cache to keep the hardware busy.
Why inference matters
Inference is where the ongoing cost and the ongoing risk of a model live. Training cost is paid once. Inference cost is paid per request for as long as the model runs, and it scales with usage. A company that rents inference per token pays more as adoption succeeds. A company that runs inference on its own hardware pays a fixed cost and benefits from every additional request. Which arrangement is cheaper depends on volume and on how steady that volume is.
Inference is also the point at which data leaves the company’s control if the model is hosted elsewhere. Every prompt, document, and record sent for inference is a disclosure to whoever operates the endpoint.
Inference and private AI
Private AI means running inference on infrastructure the company controls, using open-weight models, so that customer records, contracts, and patient data never leave the environment. A regional hospital system that uses a language model to draft discharge summaries can run inference on a single GPU server inside its own network. FlatClaw, the open-source private AI platform from Kirk Tech Solutions, is built for this arrangement and runs the inference server, the agents that call it, and the access controls around both.
What to watch for
- Measure inference latency and throughput under realistic load, because an idle server reveals little.
- Decide between batch inference and real-time inference per use case, since most business predictions do not need an instant answer.
- Log every inference request and response, which is the basis for monitoring, auditing, and later retraining.
- Check where inference physically runs and who can see the inputs before signing any contract.