What is Inference? Definition & Guide

What is Inference?

nference is the process of running a trained AI model to generate outputs from new inputs. Training creates the model; inference is when the model is put to work. Enterprise AI deployments are primarily concerned with inference performance: latency (response time), throughput (requests per second), cost per query, and reliability. Optimising inference for on-premise deployment often involves quantization, distillation, and batching.

Inference optimisation is a distinct engineering discipline from model training. The training team optimises for model quality; the inference team optimises for serving that model at production scale. Key inference engineering decisions include batching strategy (grouping requests to maximise GPU utilisation), precision (running inference in FP16 or INT8 rather than FP32 to reduce memory and compute requirements), KV cache management (reusing computation for the context portion of prompts that repeat across requests), and model parallelism (splitting large models across multiple GPUs to fit within VRAM constraints).

For enterprise AI deployments, inference infrastructure choices determine total cost of ownership and operational complexity. Managed inference services (AWS Bedrock, Azure OpenAI, Google Vertex AI) simplify operations but send data outside the enterprise perimeter. Self-managed inference (vLLM, TGI, Ollama on enterprise hardware) provides data sovereignty and long-term cost predictability, but requires inference engineering expertise. Hybrid approaches — running smaller models on-premise for common tasks and routing complex tasks to a managed service — are increasingly common in enterprises that need both sovereignty and peak capability.

Also known as: Model Inference, Prediction

Key Points

Core idea
Inference optimisation balances latency (how fast is a single response), throughput (how many requests per second), and cost per request. Improving one often involves trade-offs against the others.
Why it matters
Processing multiple requests simultaneously on the same GPU (batching) dramatically improves throughput and reduces cost per request, though it adds small amounts of latency for individual requests.
Enterprise use
When the system prompt or document context is the same across many requests, caching the computed KV (key-value) state for that portion eliminates redundant computation and reduces latency.

How It Works

How Inference works

Define the purpose, inputs, and success criteria that Inference must support.
Apply Inference in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

Low-latency inference on customer infrastructure.

Fluid AI's inference layer is optimised for low-latency, high-throughput production workloads running entirely on customer-owned hardware, without cloud API dependencies.

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Topics Covered

LLM inference enterprise
on-premise AI inference
AI inference latency optimization
model serving enterprise AI
vLLM TGI enterprise
AI inference cost optimization
GPU inference enterprise AI
self-hosted LLM inference

Inference