> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # Models > Which models and GPU types you can dispatch on Hosted Training The model list is cluster-dependent and changes as models are added, so the CLI is the source of truth: ```bash theme={null} prime train models ``` 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](/hosted-training/billing). ## 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: ```bash theme={null} prime train models --fft-only ``` If the model you want is missing, [reach out](mailto:contact@primeintellect.ai) to get it added. ## GPU types To list every GPU type you can dispatch on: ```bash theme={null} prime train gpus ``` 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. ## 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](/hosted-training/full-finetuning#config) for single-node and multi-node examples. ## 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](/prime-rl/configuration). How GPU-hours are metered and where rates come from. Config schema and dispatch reference. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.