Deployment
A serving system practice that turns a trained model plus its runtime stack into a live, versioned inference service on real hardware.
Opening summary
Deployment is the step where a trained model stops being only a checkpoint and becomes a service other systems can actually call. The job is not just to copy weights onto a machine. The job is to fit the model, its runtime, its key-value (KV) cache, its traffic policy, and its rollback plan into hardware that stays reliable under real traffic.
At a glance
Released
November 2016
System type
Serving
Related models
Related modules
No related modules listed yet.
What It Is
Deployment means turning a trained model and its serving stack into a live inference service. The stack usually includes the weights, tokenizer, runtime, memory plan, request path, monitoring, and release policy needed to answer real requests safely.Where It Sits
It sits after training and after architecture design. Training changes the weights. Architecture design decides what kind of model exists. Deployment does neither. Deployment decides whether that finished model can run acceptably on the target hardware, under the target latency and memory limits, with a release path that can move forward or roll back safely.How It Works
Teams package a model with a serving runtime, check that the model and its live state fit the chosen hardware, then expose the system behind a controlled rollout. Hardware shape matters because memory size, memory bandwidth, and accelerator count change how much model state can stay resident. Quantization, batching choices, and KV-cache strategy matter because they change whether the service can hold enough concurrent work without running out of memory or missing latency goals.Deployment System Flow
Package model and runtimeCheck hardware and memory fitRoll traffic forward carefullyKeep a fast rollback pathPackage model and runtime to Check hardware and memory fitCheck hardware and memory fit to Roll traffic forward carefullyRoll traffic forward carefully to Keep a fast rollback path
Request and weight flow
Control flow