⌘K
ENES
GitHub
conceptInference Systems~1 min readUpdated 2026-06-07#inference#serving#vllm#tgi#tensorrt-llm

Serving engines

A serving engine is the runtime that turns a model checkpoint into a production API. It handles batching, scheduling, KV cache, kernels, streaming, quantization support, metrics, and operational integration.

Common engines

Engine Strength
vLLM high-throughput serving, PagedAttention, OpenAI-compatible APIs
TGI Hugging Face ecosystem integration and production serving features
TensorRT-LLM NVIDIA-optimized inference and deployment stack
llama.cpp-style runtimes local and CPU/GPU edge deployment
Managed APIs operational simplicity, less hardware control

Selection criteria

  • Model architecture and weight format support.
  • Quantization support.
  • Continuous batching and KV-cache strategy.
  • Streaming API and structured-output compatibility.
  • Metrics, tracing, and autoscaling integration.
  • Hardware support and deployment environment.
  • Team ability to operate and debug it.

Production concerns

Serving engines still need capacity planning, safety checks, rate limits, model versioning, rollback, observability, and data governance. The runtime does not replace the product's release process.

Pitfall

Do not choose an engine from a benchmark headline alone. Match it to your model, traffic shape, latency target, hardware, and operational maturity.

Connects to: serving and inference · KV cache · batching

Related notes

Inference SystemsBatching for LLM servingBatching improves GPU utilization, but LLM serving needs dynamic and continuous batching because requests have different prompt and output lengths.Inference SystemsLatency vs throughputLLM serving separates user-facing latency from system throughput, with different bottlenecks in prefill, decode, TTFT, and tokens per second.Inference SystemsWhy inference is the real costTraining is a large upfront expense, but inference repeats for every user request and often dominates product unit economics.Inference SystemsCost modeling for LLM servingLLM serving cost models connect traffic, token counts, model size, hardware, utilization, cache rate, retries, and quality gates.