What Is the Inference Serving Stack?
The inference serving stack is the set of software layers that turn raw GPUs into a production serving endpoint. From the bottom up:
| Layer | Examples |
|---|---|
| Drivers and runtime | NVIDIA driver, CUDA |
| Communication libraries | NCCL, RDMA transports |
| Inference engines | vLLM, TensorRT-LLM, SGLang |
| Orchestration | Kubernetes, NVIDIA Dynamo, Grove |
| Packaging and routing | Images, model artifacts, gateways, autoscalers |
Each layer has to be compatible with the ones below it, and each one affects the throughput and latency of the endpoint on top.
Why It Is Continuous Work
Every layer ships updates on its own schedule, and each release can move the performance frontier for better or worse. A serving config that was optimal last month can leave throughput on the table this month because an engine shipped a better kernel, a better scheduler, or support for a new quantization format.
The reverse also happens. An upgrade can regress a specific model, break a driver pairing, or change a sampling default in a way that shows up as an accuracy complaint rather than an error.
Keeping the stack current without dropping live traffic is the real operational problem. It means qualifying new versions against a benchmark, rolling them out behind traffic shifting, and being able to roll back a version that regresses. A large share of an operator’s cost per token is won or lost here, on hardware that never changed.
