LLM Inference Serving
LLM inference serving operates language models as services that accept requests and generate outputs under concurrent demand. It combines model execution with tokenization, scheduling, streaming and resource management, allowing practitioners to meet latency and capacity requirements while maintaining a stable request contract and controlled access.
What it is
A serving system loads model artifacts and prepares each request before executing prompt processing and iterative generation. It schedules multiple sequences, manages attention state and returns outputs or streams tokens. Engines differ in supported architectures, precision, batching and distributed execution. The service surrounding the engine also handles authentication, limits, cancellation and errors. This distinguishes serving from offline inference on a local script and from an API gateway that forwards requests to another server. End-to-end behavior depends on both layers: an efficient engine can still sit behind a slow or overloaded request path.
What the work involves
The practitioner chooses an engine compatible with the model and hardware, sets context and generation limits and tests the external API contract. Workload measurement includes prompt lengths, output lengths and arrival patterns. Useful outputs include a serving configuration, capacity test and operational dashboard. Streaming and cancellation should release resources correctly. Quality evaluation uses the deployed precision and decoding settings, while load tests examine latency distribution, error behavior and admission control under demand exceeding available capacity.
Illustrative example
A team serves a model for short chat replies and long document summaries. It configures request limits and scheduling, then runs a mixed load test rather than benchmarking one prompt repeatedly. The test checks time to first token, completion time and cancelled requests. A long request that fills the context must fail with an understandable error or be handled by an explicit application policy, without destabilizing unrelated conversations sharing the engine.
Limits and common mistakes
Peak throughput does not establish acceptable interactive latency or reliability. Model memory, KV state and runtime overhead all constrain capacity. API compatibility may cover only a subset of another provider's behavior. Quality requires representative load, tested errors and evaluation of the actual deployed model configuration. Serving infrastructure cannot correct missing knowledge or invalid tool reasoning, and operational scaling should not be confused with improvement in the model's task competence.
Prerequisites
vLLM implements PagedAttention and KV cache management for Transformers — understanding what KV cache is requires Transformer knowledge
- mediumDocker
Production vLLM deployment typically runs in containers
Related skills
- ← is part of: Inference Optimization
- ← is part of: LLM Decoding Strategies
- → is subcategory of: MLOps
- ← is part of: Model Quantization
- ← is an instance of: Ollama
- ← is an instance of: Ray Serve
- ← is subcategory of: Serverless AI
- ← is an instance of: vLLM
- ← is an instance of: ONNX Runtime
- ← is an instance of: NVIDIA Triton Inference Server
- ← is an instance of: TorchServe
- ← is an instance of: TensorRT-LLM
Sources and further reading
- vLLM online serving API
Documents the request and generation interface exposed by an LLM serving engine and its compatibility limits.
- Ray Serve LLM architecture
Explains ingress, engine instances and distributed request handling around model execution.
Last updated: 2026-10-10