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:

• Co-designs the serving backend and the prompting frontend, giving you finer control over multi-step or branching generations
• Uses RadixAttention to reuse the KV cache across requests that share a prefix
• Supports structured outputs and constrained decoding, which are useful for tool calls and JSON-shaped outputs
• Serves diffusion models for image and video workloads through SGLang Diffusion
• Offers a server mode and an in-process engine, similar to vLLM's library and standalone options

The SGLang GitHub repository is the best starting point, and its docs cover the frontend language in more depth.

3. MAX

MAX is Modular's serving framework, and it gives you control down to the kernel level.

The following are some useful features of MAX:

• Runs on both CPUs and GPUs, which helps if your fleet is not GPU-only
• Allows customization at the model and kernel level, so teams with custom architectures can add support themselves
• Serves diffusion models alongside standard LLM serving
• Exposes the same category of tuning options as vLLM and SGLang, such as batch size, memory utilization, and max sequence length, through its max serve command

The MAX documentation shows how the serving flags map to your hardware.

4. TensorRT-LLM

TensorRT-LLM is NVIDIA's inference library for its own GPUs, and if your fleet is all NVIDIA, that focus tends to pay off in speed.

The following features make TensorRT-LLM worth the setup cost:

• Builds on NVIDIA's TensorRT ecosystem, which targets low-latency inference on NVIDIA silicon
• Supports in-flight batching and a paged KV cache with block reuse
• Supports low-precision quantization, including FP8 on H100 and later GPUs, FP4 on Blackwell GPUs, and INT4 activation-aware weight quantization (AWQ)
• Fits into NVIDIA-centric MLOps stacks, including Triton Inference Server through its dedicated backend

The NVIDIA TensorRT-LLM repository has the build instructions and model support matrix you'll need before committing to it.

5. LMDeploy

LMDeploy is a toolkit for compressing, deploying, and serving LLMs, with a focus on decoding throughput and quantization.

The key features of LMDeploy include:

• Runs two inference engines: TurboMind, a C++/CUDA engine, and a PyTorch-based engine
• Uses a persistent batch (its term for continuous batching) and a blocked KV cache to serve concurrent requests
• Supports weight-only quantization such as 4-bit AWQ and INT8/INT4 KV cache quantization, which lowers memory use for larger models
• Supports automatic prefix caching alongside quantization
• Provides an offline pipeline API and an API server, so you can benchmark it against vLLM on your own model and hardware with the same setup

Start with the LMDeploy quickstart guide. Its GitHub repository documents the supported model list and quantization options.

6. llama.cpp

llama.cpp is a lightweight runtime written in plain C/C++ with no external dependencies. It supports many model families, including Llama, Qwen, DeepSeek, and Mistral.

What makes llama.cpp useful:

• Runs on a wide range of hardware, including consumer GPUs and CPU-only machines
• Keeps the dependency footprint minimal, which simplifies deployment on constrained devices
• Includes llama-server, an OpenAI-compatible HTTP server with parallel decoding and continuous batching
• Suits low-latency, single-request scenarios where a full serving stack would be more than you need

The llama.cpp GitHub page is the fastest way to get a model running locally in minutes.

7. Ollama

Ollama packages model download, versioning, and local serving behind a simple CLI and API, which makes it a common first inference tool for developers.

Why Ollama is super helpful for running and serving LLMs locally:

• Requires almost no setup, making it the shortest path from "I have a model" to "I have a running endpoint"
• Handles model pulling, versioning, and local serving through a clean CLI
• Processes parallel requests per model through the OLLAMA_NUM_PARALLEL setting and queues additional requests up to OLLAMA_MAX_QUEUE
• Divides memory into fixed parallel slots, so memory use grows with the parallel setting multiplied by the context length
• Suits single-user and small-team workloads; for many simultaneous users, vLLM or SGLang add paged KV cache management and continuous batching

The Ollama documentation walks through installation, the model library, and concurrency settings.

8. MLC-LLM

MLC-LLM is a compiler-based engine built on Apache TVM, and it fits when your target hardware is unusual or your deployment spans multiple platforms.

The following features make MLC-LLM different from the rest of this list:

• Compiles model libraries with Apache TVM, either just-in-time when the engine launches or ahead of time for web, iOS, and Android targets
• Supports AMD, NVIDIA, Apple, and Intel GPUs
• Runs on Linux, Windows, macOS, iOS, Android, and web browsers from the same toolchain
• Provides an OpenAI-compatible REST server with a server mode that raises request concurrency on dedicated GPUs
• Suits teams shipping the same model across different device types, where one runtime has to cover all of them

The MLC-LLM documentation covers weight conversion and model compilation, which web, iOS, and Android deployments need before the first run.

Wrapping Up

Here's a summary of what we've covered in this article:

Choose based on your traffic pattern, hardware, and deployment targets, and measure latency and cost on your own model.

Happy building and experimenting!
 
 

Bala Priya is a developer, educator, and technical writer based in India. She writes about programming, data science, machine learning, and AI, with a focus on making complex technical ideas practical and approachable. She creates tutorials, how-to guides, and deep dives that help developers build, solve problems, and understand complex technical concepts. Her technical interests include LLM applications, agentic AI, graph algorithms, computer vision, explainable AI, and ML systems. When she’s not writing or coding, Bala is usually reading classic literature, epic fantasy, or speculative fiction... with a cup of coffee close by. She also enjoys talking about books, learning new things, and sharing what she learns with the developer community.