Search and RAG

Qwen3 Embedding / Reranker

Embeddings and rerankers based on Qwen3, among the best open ones for multilingual search, including Russian. VL versions search images, screenshots and video.

Developer
Alibaba (Qwen), China
First release
Jun 2025
Latest release
Jan 2026
Sizes
0.6B – 8B
License
Commercial use allowedApache 2.0
Russian
Supported
Ready-made builds
GGUF
Running
Available in OllamaAlso runs without a GPU
Industries
Software development, Customer support, Documents and accounting
Deploy on your server Hugging Face GitHub

What it does

  • Knowledge base search for RAG
  • Reranking results before answering
  • Search across scans, slides and screenshots

Where it is used

Customer supportLegal and document managementE-commerce

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. Qwen3-VL-Embedding и VL-Reranker
  2. Qwen3-Embedding и Reranker

How to run it

With Ollama — the fastest wayInstall Ollama on your computer or server and find the model in its library by name. Good enough to try the model on your own tasks in one evening.Find it in the Ollama library
On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems. Quantization compresses a model so it takes less video memory and runs on more modest hardware. Answers change slightly, so quality is checked on your own examples.

Frequently asked questions

Can Qwen3 Embedding / Reranker be used in a commercial project?

Yes. License: Apache 2.0. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does Qwen3 Embedding / Reranker need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does Qwen3 Embedding / Reranker support Russian?

Yes, Russian is listed on the model card.

Where can I download Qwen3 Embedding / Reranker and what does it cost?

The Qwen3 Embedding / Reranker weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

Comparisons

Similar models

Search and RAGBGE-M3BAAI · ChinaCommercial use allowed

A model for meaning-based search in about a hundred languages. The core of RAG: the bot finds the right part of a document before answering.

DetailsSearch and RAGGTEAlibaba · ChinaCommercial use allowed

Alibaba embeddings and rerankers for search: from tiny to 7B based on Qwen2. There is a multilingual mGTE version with long context.

DetailsSearch and RAGFRIDA / Giga-EmbeddingsSber (SberDevices) · RussiaCommercial use allowed

Sber embeddings built for Russian: according to the developers, among the best on Russian-language search benchmarks. FRIDA is compact, Giga-Embeddings is more powerful.

Details

Source: huggingface.co/Qwen/Qwen3-VL-Embedding-8B. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.