How to Install WebThinker-QwQ-32B Locally?

by Ayush Kumar | June 9, 2025

Ready to build cheaper?

Custom CPU plans from as little as $0.012/hour.

WebThinker-QwQ-32B is a large-scale reasoning model designed to mimic human research processes. With 32 billion parameters, it autonomously navigates the web, clicking links and interacting with pages to gather information. It can draft research reports while exploring, integrating real-time knowledge acquisition with writing. Trained using reinforcement learning techniques, it optimizes its performance through iterative feedback loops, making it ideal for complex problem-solving and open-ended tasks requiring external research.

WebThinker-QwQ-32B — GPU Configuration Table

GPU Model	vCPUs	RAM (GB)	VRAM (GB)	Use Case	Recommended For
NVIDIA H100 SXM	224	1024	80	Full precision (fp16/bf16), high-throughput reasoning	✅ Best performance, research, RL-based tasks
NVIDIA A100 80GB	192	512	80	Full context length + RL reward modeling	✅ Efficient large-scale inference
NVIDIA A100 40GB	96	256	40	4-bit or 8-bit quantized inference only	⚠️ Needs quantization (e.g., bitsandbytes)
RTX 6000 Ada 48GB	64	128	48	4-bit inference with small batch size	🚫 Not recommended for full precision
RTX A6000 (48GB)	48	96	48	Low-batch 4-bit inference	❌ Only for experimentation
Dual A100 (40+40GB)	96	384	80 (total)	Multi-GPU split with `gpu-split` param	⚠️ Setup complexity; not plug-and-play

Related Models

Step-by-Step Process to Install WebThinker-QwQ-32B Locally

For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.

Step 1: Sign Up and Set Up a NodeShift Cloud Account

Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.

Follow the account setup process and provide the necessary details and information.

Step 2: Create a GPU Node (Virtual Machine)

GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.

Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy

Step 3: Select a Model, Region, and Storage

In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.

We will use 1 x H100 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.

Step 4: Select Authentication Method

There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.

Step 5: Choose an Image

Next, you will need to choose an image for your Virtual Machine. We will deploy WebThinker-QwQ-32B on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install WebThinker-QwQ-32B on your GPU Node.

After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.

Step 6: Virtual Machine Successfully Deployed

You will get visual confirmation that your node is up and running.

Step 7: Connect to GPUs using SSH

NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.

Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.

Now open your terminal and paste the proxy SSH IP or direct SSH IP.

Next, if you want to check the GPU details, run the command below:

nvidia-smi

Step 8: Check the Available Python version and Install the new version

Run the following commands to check the available Python version.

If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA.

Run the following commands to add the deadsnakes PPA:

sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update

Step 9: Install Python 3.11

Now, run the following command to install Python 3.11 or another desired version:

sudo apt install -y python3.11 python3.11-venv python3.11-dev

Step 10: Update the Default `Python3` Version

Now, run the following command to link the new Python version as the default python3:

sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2
sudo update-alternatives --config python3

Then, run the following command to verify that the new Python version is active:

python3 --version

Step 11: Install and Update Pip

Run the following command to install and update the pip:

curl -O https://bootstrap.pypa.io/get-pip.py
python3.11 get-pip.py

Then, run the following command to check the version of pip:

pip --version

Step 12: Clone the WebUI Repo

Run the following command to clone the webui repo:

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui

Step 13: Run the One-Click Installer Script

Execute the following command to run the one-click installer script:

bash start_linux.sh

It will automatically detect your GPU, install everything needed (Python, pip, CUDA toolkits, etc.)
You’ll be prompted to select your GPU backend (choose CUDA or NVIDIA GPU).
Wait for it to finish setting up Python environment + dependencies.

Since our VM uses NVIDIA CUDA GPUs (e.g., A100, H100, A6000), choose:

Just type A and hit Enter.

What Happens Next

Once you select option A, the script will:

Install torch, vllm, and GPU-specific dependencies
Prepare the web UI environment
Prompt you to select or download a model (or you can do that manually)
Launch the server on http://127.0.0.1:7860

Step 14: SSH Port Forward

On your machine run the following command for SSH port forward:

ssh -L 7860:127.0.0.1:7860 root@<your_vm_ip>

Then open: http://localhost:7860 in your browser.

Step 15: Download the Model

Run the following command to download the model:

python3 download-model.py lixiaoxi45/WebThinker-QwQ-32B

Step 16: Go back to the Web UI in browser

Go to the “Model” tab
Find lixiaoxi45/WebThinker-QwQ-32B
Click Load

Step 17: Test with Prompts

Chain-of-Thought Reasoning

You are a reasoning assistant. Please solve the following question step by step:

Question: If a train travels 120 km in 2 hours and 180 km in the next 3 hours, what is its average speed for the entire journey?

Tabular Reasoning

Here is a table:

| Product | Price | Quantity |
|---------|-------|----------|
| Pen     | 5     | 10       |
| Book    | 20    | 4        |
| Bag     | 100   | 2        |

Calculate the total cost and sort the items by their individual total value in descending order.

Conclusion

WebThinker-QwQ-32B isn’t just another large language model—it’s a thinking engine. With the ability to mimic real human research workflows by actively navigating information, clicking links, and drafting content while exploring, it represents a major step forward in open-ended reasoning and autonomous research assistance.

Thanks to platforms like NodeShift, deploying and running such powerful models locally or in the cloud is no longer a task reserved for experts. With just a few commands and a GPU-powered VM, you can bring WebThinker to life and start experimenting with deep, multi-hop reasoning tasks.

Whether you’re solving complex problems, generating structured research drafts, or exploring real-world data with precision, WebThinker-QwQ-32B is built to support you—step by step, click by click.

Relevant blog posts

June 6, 2025

How I Built a Cloud Time Series Forecaster with Datadog Toto-Open-Base-1.0

Toto is a powerful open-source foundation model built specifically for multivariate time-series forecasting, especially in observability scenarios like server metrics, system telemetry, and operational data. What makes Toto special? High-Dimensional Forecasting: It can handle multiple variables at once — like CPU, memory, disk, or any custom metrics — and predict future behavior in parallel. Decoder-Only Transformer: Toto uses a decoder-style transformer, optimized with proportional factorized space-time attention — which simply means it’s highly efficient at dealing with both long input sequences and variable-length forecasts. Zero-Shot Forecasting: You don’t need to fine-tune it on your data. Just plug in your time-series and it will generate point forecasts and uncertainty bands instantly. Quantile-Aware Predictions: It returns not just a median forecast, but also lower and upper confidence intervals, so you can understand both expected values and risk. Trained on 2 Trillion Data Points: Toto is trained on massive volumes of observability, synthetic, and public benchmark data, making it robust even in tough, real-world datasets. Whether you’re forecasting infrastructure load, predicting IoT sensor si

June 5, 2025

How to Install and Run Xiaomi MiMo-VL Locally

We are living in times when vision-language models (VLMs) are rapidly evolving with each of them having their own pros and cons and uniqueness to stand out. MiMo-VL-7B, recently launched by Xiaomi, is now trending on Hugging Face and stands out as a compact yet remarkably capable model that pushes the boundaries of multi-modal reasoning. At just 7B parameters, MiMo-VL-7B integrates a native-resolution ViT encoder that captures fine-grained visual detail, an efficient MLP projector for cross-modal alignment, and the MiMo-7B language model optimized specifically for complex reasoning. Its development spans a rigorous two-phase training pipeline: a four-stage pretraining phase that results in the SFT variant, and a post-training phase introducing Mixed On-policy Reinforcement Learning (MORL), which blends perception, grounding, reasoning, and alignment objectives in a unified framework. This thoughtful design enables MiMo-VL-7B to deliver state-of-the-art performance across general understanding, GUI tasks, and multi-modal reasoning, often outperforming larger open-source models across all benchmarks. This model is perfect if you’re building a general-purpose VLM system or need deep reasoning over images, videos, and text.

June 4, 2025

How to Install and Run Sarvam – M Locally?

Sarvam-M is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This advanced model offers exceptional improvements over its base counterpart, showcasing a +20% enhancement in Indian language benchmarks, a +21.6% improvement on math tasks, and a +17.6% boost in programming capabilities. Sarvam-M excels particularly in scenarios that involve Indian languages and mathematics, achieving impressive performance, including an +86% improvement on romanized Indian language GSM-8K benchmarks. With a hybrid thinking mode, Sarvam-M supports both complex reasoning tasks such as coding and mathematical problem-solving, as well as general-purpose conversation in a seamless manner. It is specifically designed to handle both Indic scripts and romanized versions, making it a versatile solution for multilingual environments, particularly for Indian users. The model’s superior reasoning abilities make it ideal for tasks requiring high accuracy in logical thinking, math, and coding. It can easily switch between “think” mode for complex tasks and “non-think” mode for efficient conversation.

See all posts

Ready to build
with us?

The ideal way for organizations young and old to ease their way into the distributed and affordable cloud at their own pace.

Stay Tuned!

Stay up to date with the latest updates, news, and hotfixes for our product.

NodeShift creates a vital link between developers and affordable cloud.

Switch theme

English (EN)
Arabic (AR)
Chinese (ZH-CN)
German (DE)
Korean (KO)
Russian (RU)
French (FR)
Spanish (ES)
Portuguese (PT)
Japanese (JA)

JavaScript is disabled in your browser. For a better experience, please enable JavaScript.Learn how to enable JavaScript.