8 LLM Inference Frameworks You Should Try in 2026

A model that runs in a notebook serves one request at a time. A production endpoint receives many requests at once, and each one needs GPU memory for its key-value (KV) cache, a slot in the batch, and a response stream. Plain PyTorch or Hugging Face Transformers can serve the model, but you have to build the request scheduling, cache management, and API layer around it yourself.

Inference frameworks supply those pieces. They sit between the model and your application and cover token generation, KV cache management, request batching, and the API surface, so you don't build that machinery for every model you ship. With a suitable framework, the same GPU can serve more concurrent requests at lower latency with no change to the model.

This article covers inference frameworks for high-throughput, data-center-scale serving, as well as tools designed for local, edge, and low-resource environments.

Let’s explore the frameworks.

1. vLLM

vLLM is one of the most widely adopted engines for serving large language models (LLMs) at scale, and developers moving off plain Transformers often start with it.

Here's why vLLM is great for many practical applications:

• Uses PagedAttention to manage KV cache memory in small blocks, which packs more concurrent requests onto the same GPU
• Supports continuous batching, so new requests join a running batch while earlier ones are still generating
• Ships an OpenAI-compatible server, so existing client code often needs no changes
• Runs as an in-process library through its LLM class for offline batch jobs and evaluation pipelines, as well as a standalone server
• Supports NVIDIA, AMD, and Intel GPUs, plus CPUs and TPUs

Start with the vLLM documentation, then look at its support for tensor parallelism if you're splitting a model across multiple GPUs.

2. SGLang

SGLang pairs a fast backend runtime with a frontend language for describing model interactions, which suits workloads with complex prompting logic.

What makes SGLang useful: