A parameterized model computes outputs from inputs using stored numerical parameters and a defined computation. Training chooses or adjusts those parameters through a procedure; inference executes the configured computation. A neural network's weights are not a searchable list of all its training examples. They are numerical state used in operations, and their usefulness must be evaluated on the task the model is intended to perform. [1]

A typical supervised optimization loop computes predictions, evaluates a loss, calculates gradients and uses an optimizer to update parameters. Gradients describe how the chosen objective changes with parameter changes under the computation. The optimizer applies an update rule, which can involve additional state. PyTorch's introductory optimization tutorial demonstrates these stages separately rather than treating training as one opaque operation. [1]

The loss is a mathematical objective, not a universal score for usefulness. A model can reduce training loss while performing poorly on different data or an important real-world subgroup. Validation data, evaluation procedures and deployment monitoring provide different evidence. Infrastructure throughput should not be reported as proof that training produced a suitable model.

A batch groups examples processed together for a computation or update arrangement. An epoch conventionally describes a pass through a training dataset, but sampling and streaming systems require explicit definitions. Distributed data-parallel training assigns different data to workers and synchronizes the relevant updates. Increasing worker count can also change global batch size unless the experiment controls it. [1][2]

Training can retain activations for backpropagation, gradients and optimizer state in addition to model weights. Checkpoints intended for resuming training may need optimizer and progress state as well as parameters. Inference avoids the normal update loop, but still uses intermediate allocations and application-specific state. A model-weight file size is not a complete memory estimate for either workflow. [3][1]

Autoregressive language-model serving can retain a key-value cache to avoid recomputing attention state for every earlier token. Cache requirements depend on architecture, representation, sequence length and active requests. Longer contexts and more concurrent sessions can change memory demand after the model has successfully loaded. A startup test is therefore not a full capacity test. [4]

For an autoregressive language model, prefill processes the input prompt and establishes state used for generation. Decode then generates subsequent tokens iteratively. These phases can stress computation and memory differently. Serving performance therefore depends on prompt lengths, output lengths, batching, concurrency and scheduling rather than only the GPU model. [5]

Time to first token measures one part of the user's wait. Time between subsequent tokens and total response time describe other parts. Aggregate tokens per second counts output across the service and can rise while a particular user's response becomes slower. A service should state which latency and throughput measures define an acceptable experience. [5]

Fine-tuning starts from an existing model and updates selected parameters for an additional objective or dataset. It can update the full parameter set or use a parameter-efficient method. The distinction concerns what is trained and how, not whether learning somehow becomes inference. Parameter-efficient approaches can reduce trainable state but do not automatically remove the memory and computation required to execute the base model. [6]

Providing retrieved documents in a prompt is another mechanism. It changes the input context for an inference request rather than necessarily changing model weights. Both approaches can be useful, but they solve different problems and create different data-management responsibilities. Do not describe every form of customization as fine-tuning or assume a new prompt permanently teaches the model.

Mixed precision and quantization can change representation, memory traffic and supported arithmetic. Frameworks provide specific mechanisms rather than a universal switch that makes all calculations equally accurate at fewer bits. Some stages may need different precision from others. Evaluate the resulting model and application, not just the smaller checkpoint or improved kernel timing. [7][8]

For training, record model identity, trainable parameters, data, precision, optimization settings, batch structure, parallelization and recovery needs. For inference, record model version, input/output distributions, concurrency, latency objectives, cache policy and quality requirements. Then measure memory, throughput, sustained behavior and total time at that configuration.

The lasting distinction is simple but powerful: training changes selected model state through optimization, while inference applies configured model state to inputs. The surrounding systems can be complex, but naming those phases correctly prevents confused estimates of memory, compute cost and product capability. A good infrastructure decision starts with the actual experiment or service, not the generic label AI workload.