Train
Choose a Recipe
Pre-configured training setups that abstract away base model selection and training parameters.
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.
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
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.
| Tier | Best for |
|---|---|
| Tiny | High-throughput tasks where speed matters most — simple classification, extraction, tagging |
| Small | Fast inference with more capability — structured output, entity extraction, routing |
| Medium | Good balance of speed and intelligence — summarization, Q&A, agentic tasks that need to be fast |
| Large | Complex reasoning, multi-step tasks, difficult agentic use cases |
Some recipes offer specific capabilities (like multimodal support) that are only available with certain base models. Choose those when your task requires them.
Available recipes
These are the pre-built recipes on the platform, each configured and tested by the Inference team. Your project's own recipes appear alongside these in the recipe picker.
| Recipe | Base Model | Parameters | Compute | Description |
|---|---|---|---|---|
| Tiny | Qwen 3.5 0.8B | 0.8B | 8x H100 | Tiny and incredibly fast. Best for high-volume, low-complexity tasks. |
| Small | Qwen 3.5 4B | 4B | 8x H100 | Small and incredibly fast. A good default when latency is the priority. |
| Medium | Qwen 3.5 9B | 9B | 8x H100 | Good balance of speed and intelligence. Good as a fast agentic model. |
| Large | Qwen 3.5 27B | 27B | 8x H200 | Large and intelligent. Great for difficult agentic use cases. |
All recipes run on 8 GPUs and include optimized training parameters: the Tiny, Small, and Medium tiers use 8x H100, and Large uses 8x H200. You don't need to configure any of this — just pick the tier that fits your task.