Models · Qwen · Embeddings

Deploy Qwen3-Embedding-4B

Qwen/Qwen3-Embedding-4BdedicatedHugging Face ↗

Qwen3-Embedding-4B by Qwen 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
Qwen
Params
4.0B
Context
40K
Architecture
Dense
License
Apache 2.0
Released
2025-06-03

Footprint

Checkpoint
8.0 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

Apache 2.0Commercial use allowed

Permissive: commercial use, modification, and private deployment allowed; keep the license and notices. Read the license. Not legal advice.

FAQ

Can I run Qwen3-Embedding-4B on a dedicated deployment in Europe?

Yes. sference deploys Qwen3-Embedding-4B 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 Qwen3-Embedding-4B need?

The published checkpoint is about 8.0 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 Qwen3-Embedding-4B licensed for commercial use?

Qwen3-Embedding-4B is published under the Apache 2.0 license. Permissive: commercial use, modification, and private deployment allowed; keep the license and notices.

How is a dedicated deployment priced?

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

Related models

Request Qwen3-Embedding-4B

Tell us roughly how much traffic you expect. A fine-tune of Qwen3-Embedding-4B? 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.

No commitment. Prefer email? [email protected]

Specs from Hugging Face, synced 2026-10-27. Machine-readable version: https://sference.com/models/Qwen/Qwen3-Embedding-4B.md