# Deploy inclusionAI models on dedicated GPUs

5 inclusionAI models ready to deploy on GPU capacity reserved for you, from 7.9B to 1000B parameters, covering chat & reasoning and vision & multimodal. Private OpenAI-compatible endpoint, European GPUs, billed per GPU-hour.

| Model | Type | Params | Context | License | Page |
| --- | --- | --- | --- | --- | --- |
| Ling-3.0-tiny | Chat & reasoning | 7.9B | 128K | MIT | https://sference.com/models/inclusionAI/Ling-3.0-tiny.md |
| Ling-3.0-flash | Chat & reasoning | 127B | 256K | MIT | https://sference.com/models/inclusionAI/Ling-3.0-flash.md |
| Ling-1T | Chat & reasoning | 1000B | 32K | MIT | https://sference.com/models/inclusionAI/Ling-1T.md |
| Ling-2.6-flash | Chat & reasoning | 107B | 128K | MIT | https://sference.com/models/inclusionAI/Ling-2.6-flash.md |
| Ling-3.0-flash-VL | Vision & multimodal | 125B | 128K | MIT | https://sference.com/models/inclusionAI/Ling-3.0-flash-VL.md |

## FAQ

### Which inclusionAI models can I deploy on sference?

5 today, most requested first: Ling-3.0-tiny, Ling-3.0-flash, Ling-1T, Ling-2.6-flash and Ling-3.0-flash-VL. Any other inclusionAI checkpoint or a fine-tune of one can be requested with the form on this page.

### How many GPUs do inclusionAI models need?

It depends on the model: the lineup runs from 7.9B to 1000B parameters. Each model page lists the checkpoint size and the minimum footprint on 80 GB and 141 GB GPUs. You don't pick hardware on sference: you choose a performance tier and we size the deployment.

### Can I use inclusionAI models commercially?

5 of the 5 listed models are under licenses that allow commercial use (MIT across the lineup). Each model page summarizes its license and links the full text.

### How is a dedicated deployment priced?

Per GPU-hour on a monthly commitment, sized to your throughput and latency targets.

Request a deployment: https://sference.com/models/inclusionAI#request. All makers: https://sference.com/dedicated.md