Inference

Inference is the phase of the machine learning lifecycle where a trained model processes new, previously unseen input data to generate predictions, classifications, or generated content. While training is a computationally intensive, typically one-time process that adjusts model parameters to learn from data, inference is the ongoing operational phase where the model delivers value by serving requests in production applications.

For large language models, inference involves processing an input prompt through the model architecture to generate output tokens one at a time in an autoregressive manner. Each generated token requires a forward pass through the entire model, making inference latency and throughput critical performance metrics. The time-to-first-token (TTFT) measures how quickly the model begins responding, while tokens-per-second measures the sustained generation speed.

Optimizing inference performance is a major engineering challenge and cost center for AI deployments. Key optimization techniques include quantization, which reduces model weight precision from 16-bit to 8-bit or 4-bit formats; continuous batching, which groups multiple requests to maximize GPU utilization; KV-cache management, which avoids redundant computation of attention for previously processed tokens; speculative decoding, which uses a smaller draft model to propose tokens verified by the larger model; and tensor parallelism, which distributes model layers across multiple GPUs for faster processing.

Inference infrastructure decisions have direct business impact through their effect on latency, throughput, cost, and availability. Organizations must choose between cloud API services offering simplicity but limited control, managed inference platforms balancing convenience with customization, and self-hosted infrastructure providing maximum control at higher operational complexity. The optimal choice depends on factors including request volume, latency requirements, data sensitivity, cost constraints, and the specific models being served.