8 LLM Inference Frameworks Compared: What Developers Should Know

Looking to deploy large language models efficiently? Explore eight popular LLM inference frameworks and understand how they help developers improve serving performance, optimise hardware utilisation, manage workloads and build scalable AI applications.

vLLM – High-Performance LLM Serving

vLLM is an open-source inference and serving engine designed for high-throughput language model deployment. Its PagedAttention technology helps manage key-value cache memory efficiently, supporting concurrent requests and continuous batching. Developers use it to serve popular open-source models through APIs while improving GPU utilisation and handling demanding production workloads.

NVIDIA TensorRT-LLM – GPU-Optimised Inference

TensorRT-LLM is NVIDIA's framework for optimising large language model inference on NVIDIA GPUs. It supports techniques such as quantisation, attention optimisation and in-flight batching to improve performance. Developers building GPU-intensive AI applications can use it to optimise model execution, reduce latency and maximise hardware capabilities.

SGLang – Efficient Structured Generation

SGLang is an open-source framework designed for efficient LLM serving and structured generation. It includes RadixAttention for managing reusable prefix caches and supports complex generation workflows. Developers working with multi-turn conversations, tool calling and reasoning models can use its scheduling and caching capabilities to improve inference efficiency.

llama.cpp – Lightweight Local Inference

llama.cpp enables developers to run large language models locally across different hardware platforms. It supports quantised models, CPU execution and GPU acceleration, making it useful for resource-constrained environments. Its lightweight architecture is particularly attractive for desktop applications, edge computing, experimentation and privacy-focused AI deployments without requiring expensive cloud infrastructure.

Hugging Face TGI – Production Model Serving

Text Generation Inference (TGI) is a toolkit for deploying and serving transformer language models through production-ready APIs. It offers features such as continuous batching, streaming responses and tensor parallelism. Developers can integrate models into existing applications while managing inference workloads, although newer deployments should also evaluate Hugging Face's newer serving ecosystem.

DeepSpeed-FastGen – Scalable AI Inference

DeepSpeed-FastGen builds on Microsoft's DeepSpeed ecosystem to support efficient large-scale generative AI inference. Its capabilities include advanced batching and memory optimisation techniques for demanding workloads. Developers exploring distributed inference can investigate its approach to scaling model serving across GPU resources, while checking current project maintenance and compatibility