triton-inference-server/server

★ 10,657PythonAudience · developerComplexity · 4/5LicenseSetup · hard

Mindmap

mindmap
  root((Triton Server))
    What it does
      Serve AI models
      Handle request batching
      Schedule GPU work
    Tech stack
      Python
      Docker
      gRPC and REST
      CUDA
    Use cases
      Production inference
      Multi-framework serving
      Edge deployment
    Audience
      ML engineers
      DevOps teams
      Cloud architects

mindmap root((Triton Server)) What it does Serve AI models Handle request batching Schedule GPU work Tech stack Python Docker gRPC and REST CUDA Use cases Production inference Multi-framework serving Edge deployment Audience ML engineers DevOps teams Cloud architects

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Things people build with this

USE CASE 1

Deploy a PyTorch or TensorFlow model as a production API endpoint that handles thousands of requests per second.

USE CASE 2

Run multiple AI models on the same GPU hardware simultaneously to maximize inference throughput.

USE CASE 3

Serve voice recognition models with sequence batching to maintain conversation context across requests.

USE CASE 4

Build a Kubernetes-based AI inference cluster on major cloud providers using the included deployment examples.

Tech stack

PythonC++DockergRPCHTTP/RESTTensorRTONNXCUDA

Getting it running

Difficulty · hard Time to first run · 1h+

Requires an NVIDIA GPU with CUDA drivers and Docker, model repository must match Triton's expected directory layout.

Use freely for any purpose including commercial products under BSD license terms.

In plain English

Triton Inference Server is software from NVIDIA that serves AI models over a network, letting applications send requests to a model and receive predictions in response. The problem it solves is common in production AI deployments: you have a trained model, and you need a reliable, high-performance way for other software to use it. Triton acts as that serving layer, handling the networking, queuing, batching, and scheduling so your application does not have to. One notable feature is that Triton works with models from many different training frameworks. Whether a model was built with PyTorch, converted to the ONNX format, optimized with NVIDIA's TensorRT, or written as a Python script, Triton can serve it. This matters in organizations where different teams use different tools: Triton becomes a common serving interface regardless of how each model was trained. Performance is a central focus. When many requests arrive at once, Triton can group them into batches and run them together on a GPU, which is much faster than processing each request individually. It also supports running multiple model instances at the same time to use all available hardware. For stateful models that need to track context across multiple requests, such as voice recognition systems, it provides sequence batching to keep related requests together. The server exposes its API using standard HTTP/REST and gRPC protocols, following a community specification called KServe. There is also a C API for embedding Triton directly into an application without the network layer, which suits edge deployments where low latency is important. Deployment is typically done through Docker images pulled from NVIDIA's cloud registry, and the README includes Kubernetes deployment examples for major cloud providers. Triton is open source and available under a BSD license. It is also included in NVIDIA's commercial AI Enterprise platform for organizations that want paid support.

Copy-paste prompts

Prompt 1

I have a trained PyTorch model. Write me a Triton model repository layout with config.pbtxt that enables dynamic batching on a single GPU.

Prompt 2

Show me how to write a Python gRPC client that sends images to Triton Inference Server and processes the response.

Prompt 3

Generate a Docker Compose file for running Triton with a mounted model repository and a health-check endpoint.

Prompt 4

I have a TensorRT engine file. Create the Triton model config that enables concurrent model instances for maximum throughput.

Prompt 5

Write a Kubernetes Deployment manifest for Triton Inference Server on GKE, exposing the HTTP and gRPC ports.

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