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A recipe is a pre-configured training setup. It includes a vetted base model, optimized training parameters, and compute configuration. You don’t need ML expertise to pick one.

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What a recipe includes

  • Base model — selected by the Inference team for quality on the task type
  • Optimized training parameters — learning rate, epochs, and other hyperparameters
  • Compute configuration — minimum 8 GPUs per training run

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How to choose

Pick based on task difficulty and capability needs, not specific model names. Start small and scale up only if your eval results show the smaller recipe isn’t cutting it. Some recipes offer specific capabilities (like multimodal support) that are only available with certain base models. Choose those when your task requires them.

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Available recipes

These are the pre-built recipes currently available on the platform. Each one has been configured and tested by the Inference team. All recipes use 8x H100 GPUs and include optimized training parameters. You don’t need to configure any of this — just pick the tier that fits your task.