Triton Inference Server Configuration for GPU Workloads

Nvidia’s Triton Inference Server is the right engine when your workload is broader than a single LLM. It serves TensorRT, ONNX, PyTorch, and TensorFlow models through one unified API on dedicated GPU hosting. It is overkill for a single chat model. It is ideal for vision, audio, and ensemble pipelines.

Contents

• When Triton fits
• Model repository structure
• Configuration knobs
• Ensembles and BLS

When Triton Fits

Triton excels when:

• You serve multiple model types (vision + audio + text) from one server
• You need ensemble pipelines (OCR -> LLM -> speech synthesis)
• You have TensorRT-optimised models
• You need cross-framework uniformity for client code

For pure LLM serving, vLLM or SGLang are easier and faster.

Model Repository

models/
  whisper/
    1/
      model.onnx
    config.pbtxt
  yolov8/
    1/
      model.plan
    config.pbtxt
  llama3/
    1/
      model.py        # Python backend
    config.pbtxt

Each model has a versioned directory and a config.pbtxt that declares inputs, outputs, and instance count.

Config Knobs

name: "whisper"
backend: "onnxruntime"
max_batch_size: 16
input [
  { name: "audio", data_type: TYPE_FP32, dims: [-1] }
]
output [
  { name: "text", data_type: TYPE_STRING, dims: [1] }
]
instance_group [
  { count: 2, kind: KIND_GPU }
]
dynamic_batching { max_queue_delay_microseconds: 10000 }

instance_group sets how many CUDA contexts serve this model. dynamic_batching lets Triton combine requests arriving within a time window into one batch.

Ensembles

An ensemble is a named pipeline across multiple models, chained via tensor outputs. For OCR-then-LLM, you declare:

platform: "ensemble"
ensemble_scheduling {
  step [
    { model_name: "paddleocr" ... },
    { model_name: "llama3" ... }
  ]
}

Clients hit the ensemble endpoint; Triton routes internally. For more complex logic use BLS (Business Logic Scripting) via a Python backend.

See OCR-LLM pipeline and self-hosted OpenAI-compatible API for simpler alternatives when Triton’s breadth isn’t needed.