If you’re searching for state-of-the-art models for text retrieval, classification, clustering, or multilingual semantic search, Alibaba’s Qwen3 has just launched the powerful series of Embedding models in this segment. Qwen3 Embedding 8B, the largest and most powerful member of the Qwen3 Embedding family, is a model you can’t afford to ignore. This 8-billion-parameter model delivers unmatched performance in generating dense, high-quality text embeddings across over 100 languages, including programming languages. With a context window of 32k tokens and flexible embedding dimensions (up to 4096), Qwen3 Embedding 8B is not only highly scalable but also deeply adaptable to a wide range of NLP tasks. It currently ranks #1 on the MTEB multilingual leaderboard (as of June 5, 2025), setting a new benchmark in text embedding excellence. For developers or AI Engineers building advanced search engines, code retrieval systems, or multilingual recommender tools, Qwen3 8B offers both the power and flexibility to supercharge your pipeline.
In this guide, you’ll learn how to install and run Qwen3 Embedding 8B either on your local machine or in the cloud using sentence-transformers in just a few simple steps.
Prerequisites
The minimum system requirements for running this model are:
Step-by-step process to install and run Qwen3 Embedding 8B
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n qwen python=3.11 -y && conda activate qwen
Output:
2. Once you’re inside the environment, install necessary dependencies to run the model.
pip install torch torchvision torchaudio
pip install transformers>=4.51.0
pip install sentence-transformers>=2.7.0
pip install accelerate
pip install huggingface_hub
pip install bitsandbytes
pip install einops
3. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
4. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
Step 8: Download and Run the model
- Open a Python notebook inside Jupyter.
2. Download model checkpoints and run the model for inference.
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("Qwen/Qwen3-Embedding-8B")
queries = [
"What’s the difference between machine learning and traditional programming?",
"Explain gravity",
]
documents = [
"Machine learning is when a computer learns from data to make decisions or predictions, without being explicitly told what to do. Traditional programming is when a human writes specific instructions (code) that the computer must follow.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]
# Encode the queries and documents. Note that queries benefit from using a prompt
# Here we use the prompt called "query" stored under `model.prompts`, but you can
# also pass your own prompt via the `prompt` argument
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute the (cosine) similarity between the query and document embeddings
similarity = model.similarity(query_embeddings, document_embeddings)
print(similarity)
Output:
Conclusion
In this article, we explored the exceptional capabilities of Qwen3 Embedding 8B, a multilingual, high-performance model ideal for tasks like semantic search, code retrieval, and large-scale classification. With its massive parameter count, 32k context window, and flexible embedding dimensions, it stands out as a top-tier solution for modern NLP workflows. If you’re experimenting locally or deploying at scale, NodeShift cloud simplifies the process with ready-to-use environments and GPU-powered infrastructure available for both on-cloud or on-premises use cases, making it effortless to harness the full potential of Qwen3 Embedding 8B in production.
