Models · NVIDIA

Deploy NVIDIA models on dedicated GPUs

17 NVIDIA models ready to deploy on GPU capacity reserved for you, from 627M to 561B parameters, covering chat & reasoning, vision & multimodal, embeddings, rerankers, speech recognition and safety & moderation. Private OpenAI-compatible endpoint, European GPUs, billed per GPU-hour.

ModelParams
NVIDIA561B
NVIDIA638M
NVIDIA627M
NVIDIA—
NVIDIA32B
NVIDIA32B
NVIDIA979M
NVIDIA1.1B
NVIDIA33B
NVIDIA2.6B
NVIDIA8.9B
NVIDIA8.0B
NVIDIA32B
NVIDIA1.2B
NVIDIA124B
NVIDIA4.3B
NVIDIA1.2B

Specs from Hugging Face, synced 2026-10-27. Machine-readable version: https://sference.com/models/nvidia.md

FAQ

Which NVIDIA models can I deploy on sference?

17 today, most requested first: NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16, nemotron-3.5-asr-streaming-0.6b, parakeet-tdt-0.6b-v3, parakeet-tdt-0.6b-v2, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, canary-1b-v2 and Nemotron-3-Embed-1B-BF16, and 9 more. Any other NVIDIA checkpoint or a fine-tune of one can be requested with the form on this page.

How many GPUs do NVIDIA models need?

It depends on the model: the lineup runs from 627M to 561B 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 NVIDIA models commercially?

14 of the 17 listed models are under licenses that allow commercial use (OpenMDW 1.1, CC BY 4.0, NVIDIA Nemotron Open Model License, NVIDIA Open Model License, Custom license and OpenMDW 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 NVIDIA model

Tell us which NVIDIA model and roughly how much traffic you expect. Fine-tunes of these models work the same way.

  1. 01
    We size it

    A performance tier, the capacity behind it, and a quote for your volume.

  2. 02
    We benchmark it

    Bring one workload with a sample of real traffic; we come back with latency and throughput numbers.

  3. 03
    It goes live

    We provision the deployment. You call it with the same API and model id as before.

No commitment. Prefer email? [email protected]