What is Inference?
Inference (model inference) is the execution of a trained model on new input to produce a prediction, score, embedding, classification, or generated output without intentionally updating that model's parameters during the request.
Quick Facts
| Full Name | Model Inference |
|---|---|
| Created | Concept fundamental to machine learning since 1950s |
How It Works
An inference contract includes the model and preprocessing revisions, input schema, post-processing, hardware and runtime configuration, and output semantics. Workloads may be online, streaming, batch, edge, or embedded. A classifier can return one prediction per forward pass, while an autoregressive language model performs Prefill and repeated Decode steps before returning or streaming text. Performance must be measured against the deployed workload: latency, throughput, memory, errors, and cost are incomplete without prediction quality, calibration, structured-output validity, or task success. A fixed seed alone does not guarantee reproducibility across framework versions, kernels, devices, or distributed schedules.
Key Characteristics
- Applies a specific trained model revision without intentional per-request weight updates
- Includes preprocessing and post-processing as part of the behavioral contract
- Can run synchronously, asynchronously, in batches, or as a streamed response
- Uses latency, throughput, memory, error, and quality metrics defined at explicit boundaries
- May change behavior when precision, runtime, tokenizer, template, hardware, or decoding changes
- Requires versioned inputs, outputs, finish states, and reproducibility evidence for safe rollout
Common Use Cases
- Online classification, ranking, forecasting, and anomaly detection APIs
- Autoregressive text, code, image, audio, or multimodal generation
- Batch scoring and embedding generation for offline pipelines
- Low-latency prediction on mobile, edge, and embedded devices
- Production model evaluation, shadow traffic, canary rollout, and drift monitoring
Example
Loading code...Frequently Asked Questions
What is the difference between model training and inference?
Training estimates or updates model parameters from data and an objective. Inference applies a selected parameter revision to new inputs without intentionally updating those parameters during the request. Training, fine-tuning, evaluation, and inference can all happen repeatedly over a model's lifecycle; inference is not simply a one-time phase after training.
What is included in an inference pipeline?
A production pipeline normally validates and transforms input, selects an authorized model and optional adapter, schedules execution, runs the model, post-processes outputs, and records finish state, usage, errors, and version identity. Generative systems may also tokenize, build cache state, decode repeatedly, apply stopping criteria, and stream partial output.
How should inference performance be measured?
Use metrics that match the workload and an explicit boundary. Online services need latency percentiles, throughput, queue time, error rate, memory, and cost. Streaming generation also needs time to first token and inter-token latency. Batch jobs need completion time and utilization. Every comparison must retain quality, calibration, structured-output, or task-success gates.
Does quantization always make inference faster?
No. Lower precision can reduce weight memory and memory traffic, but speed depends on model shape, hardware support, kernels, batch and sequence distributions, transfer overhead, and compilation. Quantization can also change outputs. Benchmark the exact format on the target runtime and require task-quality regression checks.
Is model inference deterministic when the temperature is zero?
Not necessarily. Greedy token selection removes sampling randomness, but model revision, tokenizer, template, precision, kernels, floating-point reduction order, hardware, parallel execution, and runtime version may still affect results. Reproducibility requires a pinned execution contract and tests; a seed or temperature setting alone is insufficient.