Qwen 3 Next 80B Local AI Model: Complete Guide

The model which we all were waiting for is finally here: Qwen 3 Next 80 Billion. It has 80 billion total parameters but only 3 billion are activated during inference, which means this model can run on a CPU and also on a single GPU.

Just imagine this for a moment — a few weeks ago, it was unthinkable to run such massive models on such a platform. But now it seems possible, and I believe this is a huge milestone moment for Alibaba, similar to the DeepSeek moment we’ve seen recently.

In this guide, I will explain everything step-by-step. We’ll cover:

• What Qwen 3 Next 80B is and how it works
• Detailed architecture breakdown
• Installation and setup
• Running the model on CPU and GPU
• Key features and capabilities
• A step-by-step example of multilingual translation and problem solving
• VRAM requirements and performance
• FAQs

I will also show you how to run this model locally on both CPU and GPU, along with testing it on a hosted environment.

What is Qwen 3 Next 80B?

Qwen 3 Next 80B is a massive local AI model developed by Alibaba. It has a total of 80 billion parameters, but during inference, it only activates 3 billion parameters, making it possible to run on CPUs and even a single GPU.

This is a hybrid architecture model that combines two approaches:

1. 75% Gated DeltaNet layers – These use a specialized linear attention mechanism that is extremely efficient for handling long sequences without quadratic complexity.
2. 25% Standard Scaled Dot Product Attention layers – These are tweaked to provide strong recall capabilities, which are areas where linear attention sometimes struggles.

This combination allows the model to process long-context data efficiently while maintaining high accuracy and strong performance.

Another key component is its Mixture of Experts (MoE) architecture:

• The model has 512 experts, but only the relevant ones are activated for specific tasks.
• This means the model behaves like having a massive toolbox, where only the required tools are selected based on the input.

Qwen 3 Next 80B: Overview Table

Key Features

• Run on CPU or single GPU due to its efficient parameter activation.

• Hybrid Attention Architecture:

  • Linear attention for long sequences.
  • Standard attention for strong recall capabilities.

• Mixture of Experts (MoE): Only the needed experts are activated per task.

• Zero-centered RMS Norm across the architecture to maintain stability.

• Multi-token prediction: Optimized to be consistent between training and inference.

• Cost-efficient training: Trains at less than 10% cost compared to a dense 32B model.

• Strong multilingual performance.

• Capable of solving complex mathematical and logical problems step-by-step.

Step-by-Step Guide: Installing and Running Qwen 3 Next 80B

We will now go through the installation and setup process step-by-step using an Ubuntu system.

Step 1: Create a Virtual Environment

First, create a virtual environment to keep everything organized and separate.

conda create -n qwen3next python=3.10
conda activate qwen3next

This keeps dependencies isolated and prevents conflicts with other projects.

Step 2: Install Prerequisites

Install required libraries, including Transformers, Torch, and TorchVision.

pip install transformers torch torchvision

This will take a few minutes depending on your internet speed.

Step 3: Log into Hugging Face

The model is hosted on Hugging Face. You need to log in to download it.

huggingface-cli login

• Go to your Hugging Face profile and generate a free read token.
• Paste the token when prompted.

Step 4: Launch Jupyter Notebook

Run the following command to start Jupyter Notebook:

jupyter notebook

Once it opens, import necessary libraries and set the device to CPU or GPU.

Step 5: Download the Model

The model is huge, split into 41 shards. Each shard is approximately 4 GB, requiring 200 GB of disk space.

Example code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

device = torch.device("cpu")

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-3-Next-80B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-3-Next-80B", torch_dtype=torch.float16, device_map="cpu")

Step 6: Verify Disk Space

Make sure you have sufficient storage:

df -h

The total model size will be close to 160-200 GB.

Step 7: Run Inference

Here’s an example prompt to test the model:

prompt = "What is the smallest country in the world?"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

On CPU, inference may take a long time (30-55 minutes for some prompts). For faster results, switch to GPU.

Step 8: Run on GPU

If you have a GPU, set the device map to auto:

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-3-Next-80B", device_map="auto")

• On full precision, the model consumes 68 GB of VRAM.
• Example test prompt for GPU:

prompt = "Write 10 sentences ending with the word happy."

Understanding the Architecture

The architecture of Qwen 3 Next 80B is one of its most impressive aspects.

• Zero-centered RMS Norm: Ensures stable training and inference by preventing attention layers from getting unstable.
• Mixture of Experts Router Initialization: Avoids random bias toward certain experts early in training.
• Multi-token Prediction: Designed end-to-end for consistency between training and inference.
• Cost Efficiency: Trains at less than 10% of the cost of a dense 32B model while delivering superior performance.

During inference, it activates only 3 billion parameters, making it efficient for deployment on local machines.

Testing the Model on Hosted Service

While the model downloads, you can test it on chat.co.ai:

1. Go to chat.co.ai.

2. Log in with your email for a free account.

3. From the top-left menu, select GL3 Next 80B.

4. Example prompt:

   • Create a self-contained HTML file with a rocket zooming dynamically across the screen.

The model generates functional code, which you can run directly in your browser.

Multilingual Translation Example

To test multilingual capabilities, we gave the model the following prompt:

"Translate the sentence 'Clinging to the past is like holding on to smoke' into multiple languages, including fictional languages, and provide cultural notes for three languages."

Output:

• Accurate translations in languages like Mandarin, Urdu, Japanese, Korean, Italian, Polish, Tagalog, and more.

• Runic script and fictional language translations.

• Cultural notes, such as:

  • In Japanese culture, smoke symbolizes impermanence and illusion.

This shows its strength in context-aware translation and cultural adaptation.

Performance Results

• CPU:

  • The model took 55 minutes to answer the question: "What is the smallest country in the world?"
  • Answer: Vatican City, with additional relevant information.

• GPU:

  • Consumes 68 GB of VRAM.
  • Generates outputs significantly faster than CPU.

FAQs

Q1: How much storage space is required?

You need at least 200 GB of free disk space for all 41 shards of the model.

Q2: Can this model run on a laptop?

Yes, but only if your laptop has enough storage and a strong CPU or GPU.

Q3: Does it support multilingual tasks?

Absolutely, it supports a wide range of languages and even fictional languages.

Q4: What is the VRAM requirement for GPU usage?

Approximately 68 GB VRAM in full precision.

Q5: Is Hugging Face login mandatory?

Yes, you need a Hugging Face account and a free read token to download the model.

Final Thoughts

Qwen 3 Next 80B is a powerful local AI model that pushes the boundaries of what’s possible with large-scale AI. With its hybrid architecture, Mixture of Experts design, and ability to run on both CPU and GPU, it opens new opportunities for developers and researchers to run massive models locally.

From multilingual translations to complex problem solving, this model proves its versatility. Although running it on CPU is slow, it’s impressive that such a massive model can even function on local machines.