Inference

In the AI development lifecycle, **training** is where a model learns from data, but **inference** is where the model actually works. Every time a user submits a query to an AI assistant, a document is processed by an extraction pipeline, or a fraud detection model evaluates a transaction, an inference operation is executed. The model's weights — fixed after training — are loaded into memory and applied to the new input to produce an output. For large transformer models, this is a compute-intensive process: generating a 500-word response from a 70B-parameter model requires hundreds of billions of floating-point operations per second, making inference hardware selection and optimization a first-order engineering concern.

Enterprise inference infrastructure must balance three competing requirements: **latency** (how quickly individual responses are generated), **throughput** (how many requests can be processed simultaneously), and **cost** (the hardware and energy expenditure per request). These trade-offs drive a set of standard optimization techniques: **quantization** reduces model precision from 32-bit to 8-bit or 4-bit floats to cut memory and compute requirements; **batching** processes multiple requests simultaneously to amortize fixed costs; **KV caching** stores intermediate computation results to accelerate sequential token generation; and **speculative decoding** uses a smaller model to draft tokens that the larger model validates, increasing effective throughput. Each technique involves quality-versus-efficiency trade-offs that must be calibrated against specific application requirements.

The inference deployment landscape divides into **managed API inference** — where providers like OpenAI, Anthropic, and Google handle all infrastructure — and **self-hosted inference** using platforms like **vLLM**, **TensorRT-LLM**, or **Triton Inference Server**. Managed APIs eliminate operational overhead but surrender cost control and data residency guarantees. Self-hosted inference requires GPU infrastructure investment and MLOps expertise but enables fine-grained optimization, private data handling, and long-run cost reduction at scale. A mature enterprise AI strategy typically uses managed APIs for prototyping and lower-volume workloads, transitioning to self-hosted inference for high-volume production pipelines where per-request economics justify the operational investment.