NVIDIA Triton Inference Server
An open-source inference server for deploying machine learning models from many frameworks on GPUs and CPUs.
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About NVIDIA Triton Inference Server
Triton Inference Server is NVIDIA's server for running trained machine learning models in production, in the cloud or at the edge. It can serve models built with many different ML frameworks and runs on both GPUs and CPUs.
The repository includes Docker files, deployment examples and a Python OpenAI-compatible front end. It suits engineering teams that need to put models behind a network service on their own hardware.
Strengths
- Serves models from many ML frameworks in one server
- Runs on GPUs and CPUs, in the cloud or at the edge
- Docker-based deployment files included
Limitations
- Aimed at infrastructure teams rather than individual desktop users
- Setup and tuning take real effort
Details
- Pricing
- FreeFree and open source.
- License
- Open source, license not stated
- Developer
- NVIDIA
- Platforms
- Linux, Self-hosted
- How it runs
- Self-hosted
- Account
- Not required
- Best suited for
- Teams serving ML models in production on their own infrastructure
- Categories
- AI developer tools
- Last verified
- Added
Alternatives to NVIDIA Triton Inference Server
Compare allSoftware that can replace NVIDIA Triton Inference Server for an important use case, and what changes if you switch.
KServe
A Kubernetes-native platform for serving predictive and generative AI models at scale.
KServe provides a Kubernetes-native serving layer for predictive and generative models across frameworks, installed with Helm, and requires a Kubernetes cluster to run.
OpenVINO Model Server
A self-hosted inference server for serving AI models that have been optimized with OpenVINO.
OpenVINO Model Server serves models converted to OpenVINO formats from Ubuntu or Red Hat containers, narrowing framework support compared with Triton, under Apache-2.0.
vLLM
High-throughput, memory-efficient inference and serving engine for LLMs.
vLLM focuses on high-throughput serving of language models on Linux GPUs rather than models from many ML frameworks, and uses the Apache-2.0 license.
SGLang
A serving framework for running large language models and multimodal models on your own GPUs.
SGLang serves language and multimodal models on your own GPU servers with Docker files and benchmark tooling, but does not cover classic ML frameworks.
Sonar
A self-hosted LLM inference server, formerly Aphrodite Engine, that serves Hugging Face models through OpenAI-compatible APIs.
Sonar, formerly Aphrodite Engine, serves Hugging Face language models through OpenAI-compatible APIs on NVIDIA, AMD, Intel, CPU and more, under the AGPL-3.0 license.
Xinference
An open-source inference server for running language, speech and multimodal models through one API.
Xinference serves language, speech and multimodal models behind one GPT-style API, running from a laptop to servers, rather than general ML framework models.
LocalAI
Serve language, image and speech models on your own hardware.
LocalAI serves language, image and speech models on Linux or macOS behind APIs compatible with common AI services, aimed at self-hosters rather than production infrastructure teams.
NVIDIA Triton Inference Server as an alternative
Listings that name NVIDIA Triton Inference Server as an alternative.
GPUStack
An open-source GPU cluster manager for serving AI models with vLLM and SGLang on your own hardware.
NVIDIA Triton Inference Server serves models from many ML frameworks on GPUs and CPUs with Docker-based deployment, instead of managing vLLM and SGLang across a GPU pool.
Similar software
Related functionality, not necessarily a direct replacement.
Kubeflow
A Kubernetes-native set of open-source projects for running data, ML and AI workloads.
MLflow
An open-source platform for tracing, evaluating and managing LLM applications, agents and machine learning models.
LiteLLM
A self-hosted AI gateway that calls over 100 LLM providers through one OpenAI-compatible API.
