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Deploy Nemotron-3-Embed-1B-BF16

nvidia/Nemotron-3-Embed-1B-BF16dedicatedHugging Face ↗

Nemotron-3-Embed-1B-BF16 by NVIDIA on GPU capacity reserved for you: a private OpenAI-compatible endpoint, every request processed on European GPUs and logged in your audit trail, billed per GPU-hour on a monthly commitment.

Maker
NVIDIA
Params
1.1B
Context
256K
Architecture
Dense
License
OpenMDW 1.1
Released
2026-07-14

Footprint

Checkpoint
2.3 GB
BF16
80 GB-class GPUs
1 GPU
minimum to load
141 GB-class GPUs
1 GPU
minimum to load

Minimum to load the published checkpoint with a small KV cache. Long contexts and high concurrency need more; quantizing a 16-bit checkpoint needs less. You don't pick hardware on sference: you choose a performance tier and we size and run the deployment.

License

OpenMDW 1.1Commercial use allowed

Permissive open license for model weights, data, and code: commercial use and modification allowed. Read the license. Not legal advice.

FAQ

Can I run Nemotron-3-Embed-1B-BF16 on a dedicated deployment in Europe?

Yes. sference deploys Nemotron-3-Embed-1B-BF16 on GPU capacity reserved for you, behind a private endpoint, with every request processed on European GPUs and logged in your audit trail. Tell us your expected volume and latency targets with the form on this page and we come back with a sizing and a quote.

How many GPUs does Nemotron-3-Embed-1B-BF16 need?

The published checkpoint is about 2.3 GB (BF16), so loading it takes at least 1 GPU with 80 GB of memory each, or 1 GPU with 141 GB each. Long contexts and high concurrency need more for the KV cache; quantization needs less. You don't pick hardware on sference: you choose a performance tier and we size the deployment.

Is Nemotron-3-Embed-1B-BF16 licensed for commercial use?

Nemotron-3-Embed-1B-BF16 is published under the OpenMDW 1.1 license. Permissive open license for model weights, data, and code: commercial use and modification allowed.

How is a dedicated deployment priced?

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

Related models

Request Nemotron-3-Embed-1B-BF16

Tell us roughly how much traffic you expect. A fine-tune of Nemotron-3-Embed-1B-BF16? Mention it in the notes.

  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.

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Specs from Hugging Face, synced 2026-10-27. Machine-readable version: https://sference.com/models/nvidia/Nemotron-3-Embed-1B-BF16.md