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vLLM backend for decoupled model packaging

Intent

Decouple model artifacts from inference frontend evolution so that model upgrades and frontend upgrades can proceed independently without coordinated releases.

Mechanism

Package the model as a minimal JSON config pointing to weights and tokenizer (the "vLLM backend" in Triton). At deployment time, Triton's vLLM backend dynamically generates I/O tensor specs from the config — the model author never defines them. This means:

  • Frontend upgrades that change I/O specs don't require model repackaging.
  • Model upgrades don't require frontend changes (as long as the model conforms to the HuggingFace-compatible interface).

Contrast with Python backend

The alternative (Triton's Python backend) requires explicit input/output tensor specs frozen at packaging time. Every frontend upgrade touching I/O specs requires a coordinated change to packaging code; otherwise, requests fail at runtime.

Limitations

  • Version drift hazard: Triton's vLLM backend is compiled against a specific vLLM API surface. If versions drift (e.g., a removed module), the backend fails to load entirely. Must pin compatible versions in the service image.
  • Custom model logic: Models needing custom preprocessing, postprocessing, or non-standard execution must fall back to the Python backend.

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