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The model list is cluster-dependent and changes as models are added, so the CLI is the source of truth:
That gives you two things:
  • Models — the models available to dispatch
  • On-Demand Capacity — purchasable GPU types with their current $/GPU-hour, and whether a run starts now or queues
Add --output json for machine-readable output. For pricing mechanics, see Billing.

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The weight cache

Runs boot from a cluster-side weight cache and never download weights at start-up. A model that isn’t cached can’t be dispatched — a config naming one fails at dispatch, before any GPU is reserved. To see only the cached models for a reserved cluster you hold:
If the model you want is missing, reach out to get it added.

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GPU types

To list every GPU type you can dispatch on:
You can pin one explicitly at dispatch with --gpu-type, or let the platform pick. Placement favours clusters that already have your model cached, since a cache miss means a full weight download.

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Choosing a model

Validate first. Start with the smallest model that exercises your environment end to end — a short run on a small model catches config and reward-function bugs for a fraction of the cost. Keep max_steps low and watch that the loss or reward moves at all. Then scale. Once the environment is proven, move to the model you actually care about and raise batch_size and group_size for a more stable reward signal. MoE models with small active parameter counts give strong performance per GPU-hour. Size the run with the [deployment] block — see Full Fine-Tuning for single-node and multi-node examples.

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Thinking mode

Some model families support a thinking mode that produces extended chain-of-thought before the final answer. It tends to help on tasks that benefit from multi-step reasoning — math, code, logic — at the cost of longer completions, which means more tokens generated per rollout and more wall-clock per step. Thinking is controlled through the renderer and sampling config; see prime-rl Configuration.

Billing

How GPU-hours are metered and where rates come from.

Full Fine-Tuning

Config schema and dispatch reference.