How serving works

Deploy a model once. Boardwalk manages where it runs, keeps its identity intact, and routes requests through the same API.

Shared capacity

Compatible LoRA adapters share their exact base model's weights and serving capacity. Full checkpoints run as separate model identities on managed capacity. Models used less often can share a host and wake when requested.

Placement accounts for model size, memory, traffic, and the qualified serving configuration. Large models can use multiple GPUs. You do not rent or manage an individual GPU.

Your model stays your model

Sharing hardware does not make a private model public or substitute another model for it. Requests select your checkpoint or your adapter applied to its pinned base.

If Boardwalk changes the model's representation to reduce serving cost, that candidate must pass a fidelity check against the original. If the evidence is insufficient, the original representation is used. Deployment does not require you to prepare an evaluation dataset.

Qualified before activation

Each serving configuration records its source revision, engine version, precision, context, verified capabilities, and measured prices. New configurations pass qualification before taking over from the current revision.

Qualification checks serving behavior. It does not guarantee that a model will answer every question correctly; evaluate your application on representative tasks.

Ready when requested

Boardwalk handles loading and waking automatically. Startup time depends on what is already resident and how much must be loaded. A small parked model and a large checkpoint starting on new capacity have different latency.

Read latency and cold starts for client guidance. Your token rate is fixed by the serving revision; it does not surge with fleet load.