What Are LLM Inference Serving Engines?
LLM inference serving engines are systems that run trained Large Language Models (LLMs) in production and expose them through an API or local runtime for tasks such as text generation, chat, embeddings, and agent workflows. They do not train the model. Instead, they manage the serving side of inference: loading model weights, accepting prompts, scheduling requests, executing decoding on accelerators, and returning generated tokens with predictable latency and throughput.
An inference serving engine sits between an application and the hardware that runs the model. It handles practical performance concerns such as batching, token streaming, KV-cache management, GPU memory placement, quantization, distributed execution, and request isolation. These details matter because LLM inference is often constrained by GPU memory, memory bandwidth, and the sequential nature of token generation.
In practice, the serving engine determines how efficiently an LLM can be shared by many users or applications at the same time. A strong engine improves throughput, lowers latency, reduces memory pressure, and provides operational features such as OpenAI-compatible APIs, observability, deployment controls, and scaling across multiple GPUs or nodes.
Examples
- vLLM
- Ollama
- TensorRT-LLM
- Hugging Face TGI (Text Generation Inference)
- SGLang
- LMDeploy
- MLC-LLM
- Ray Serve
- DeepSpeed
- Hugging Face Accelerate