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Shared Hosted Training for LoRA runs has been retired. The service trained a LoRA adapter on top of a shared, multi-tenant deployment and billed per token. It no longer accepts new runs, and its .toml config format is no longer dispatched.

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What to use instead

Hosted Training now runs on-demand: your run gets its own GPUs, every parameter is trained, and you’re billed per GPU-hour. No reserved cluster required. Configs use the native prime-rl schema rather than the old flat format — see Hosted RL for the config shape and End-to-End Run for a walkthrough.

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Your existing adapters

LoRA adapters from completed runs remain downloadable from the Prime platform. Adapter deployment for inference has been removed: adapters can no longer be served through the Prime Inference API, and the prime deployments CLI commands have been retired. To serve a fine-tuned model, self-host it with prime-rl on your own GPUs or on a Model Factory Cluster.

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LoRA itself isn’t going away

Only the hosted shared service was retired. LoRA remains a supported, actively maintained feature of prime-rl for both RL and SFT, which you can run on your own GPUs or on a Model Factory Cluster.

Getting Started

Launch an on-demand Hosted Training run.

LoRA in prime-rl

Train LoRA adapters on your own infrastructure.