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Inference

The process of running a trained model on new input to produce predictions, embeddings, or generated output.

한국어 English
#Deployment#Inference#LLM#Serving
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English updated 8월 29, 2026 Source article updated 8월 29, 2026 2 sources
This English page is a curated translation layer linked to the Korean source article. Community changes are currently made on the Korean source, where the full revision history and anonymous edit trail are preserved.
Inference is the execution phase of a trained machine-learning model. For a language model, inference commonly means processing an input prompt and generating one or more output tokens.

How it works

Serving performance depends on model size, hardware, precision, batching, context length, memory use, and implementation. Autoregressive generation has a prompt-processing stage and a token-by-token decoding stage with different performance characteristics.

Why it matters

Inference cost and latency determine whether a model can be used interactively, at high volume, or on edge devices. Optimization techniques include Quantization, caching, batching, and specialized kernels.

Related concepts

See also KV Cache, GPU, Quantization, and Latency and Throughput.

SOURCES

Sources

  1. MLPerf Inference BenchmarkMLCommons / arXiv
    Open source ↗
  2. Efficient Memory Management for Large Language Model Serving with PagedAttentionUC Berkeley / arXiv
    Open source ↗

KNOWLEDGE LINKS

GPU → KV Cache → Large Language Model (LLM) →

BACKLINKS

GPU ConceptsKV Cache ConceptsLatency and Throughput Concepts

KEEP EXPLORING

Related articles

Latency and ThroughputTwo core serving metrics describing response delay and the amount of work a system completes over time.Context WindowThe amount of tokenized input and generated text a model can consider within one inference context.Knowledge DistillationA training method in which a smaller student model learns from the outputs or representations of a larger teacher model.QuantizationA technique that represents model weights or activations with lower numerical precision to reduce resource use.Vector SearchSearch that retrieves items by similarity between numerical vector representations.AttentionA mechanism that lets a neural network weigh the relevance of different positions when computing a representation.
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