Deploy OpenAI models on dedicated GPUs
5 OpenAI models ready to deploy on GPU capacity reserved for you, from 809M to 117B parameters, covering chat & reasoning, speech recognition and safety & moderation. Private OpenAI-compatible endpoint, European GPUs, billed per GPU-hour.
| Model | Type | Params | Context | License |
|---|---|---|---|---|
whisper-large-v3trending OpenAI | Speech recognition | 1.5B | — | Apache 2.0 |
gpt-oss-120bMoE OpenAI | Chat & reasoning | 117B | 128K | Apache 2.0 |
gpt-oss-20bMoE OpenAI | Chat & reasoning | 21B | 128K | Apache 2.0 |
| OpenAI | Speech recognition | 809M | — | MIT |
| OpenAI | Safety & moderation | 22B | 128K | Apache 2.0 |
Specs from Hugging Face, synced 2026-10-27. Machine-readable version: https://sference.com/models/openai.md
FAQ
Which OpenAI models can I deploy on sference?
5 today, most requested first: whisper-large-v3, gpt-oss-120b, gpt-oss-20b, whisper-large-v3-turbo and gpt-oss-safeguard-20b. Any other OpenAI checkpoint or a fine-tune of one can be requested with the form on this page.
How many GPUs do OpenAI models need?
It depends on the model: the lineup runs from 809M to 117B 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 OpenAI models commercially?
5 of the 5 listed models are under licenses that allow commercial use (Apache 2.0 and 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 OpenAI model
Tell us which OpenAI model and roughly how much traffic you expect. Fine-tunes of these models work the same way.
- 01We size it
A performance tier, the capacity behind it, and a quote for your volume.
- 02We benchmark it
Bring one workload with a sample of real traffic; we come back with latency and throughput numbers.
- 03It goes live
We provision the deployment. You call it with the same API and model id as before.