Frontend

The Dynamo Frontend is the API gateway for serving LLM inference requests. It provides OpenAI-compatible HTTP endpoints and KServe gRPC endpoints, handling request preprocessing, routing, and response formatting.

Feature Matrix

Quick Start

Prerequisites

• Dynamo platform installed
• etcd and nats-server -js running
• At least one backend worker registered

HTTP Frontend

$
python -m dynamo.frontend --http-port 8000

This starts an OpenAI-compatible HTTP server with integrated pre/post processing and routing. Backends are auto-discovered when they call register_model.

The frontend does the pre and post processing. To do this it will need access to the model configuration files: config.json, tokenizer.json, tokenizer_config.json, etc. It does not need the weights.

Frontend will download the files it needs from Hugging Face, no setup is required. However we recommend setting up modelexpress-server and a shared folder such as a Kubernetes PVC. This ensures the model is only downloaded once across the whole cluster.

If the model is not available on Hugging Face, such as a private or customized model, you will need to make the model files available locally at the same file path as on the backend. The backend’s --model-path <here> will need to exist on the frontend and contain at least the configuration (JSON) files.

KServe gRPC Frontend

$
python -m dynamo.frontend --kserve-grpc-server

See the Frontend Guide for KServe-specific configuration and message formats.

Kubernetes

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apiVersion: nvidia.com/v1alpha1
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kind: DynamoGraphDeployment
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metadata:
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  name: frontend-example
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spec:
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  graphs:
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    - name: frontend
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      replicas: 1
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      services:
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        - name: Frontend
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          image: nvcr.io/nvidia/dynamo/dynamo-vllm:latest
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          command:
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            - python
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            - -m
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            - dynamo.frontend
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            - --http-port
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            - "8000"

Configuration

See the Frontend Guide for full configuration options.

Next Steps