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LatestQwen · AlibabaQwen3-Next
Qwen · AlibabaText / LLM

Qwen Releases 80B Mixture-of-Experts Model

The new Qwen3-Next model from Alibaba combines a large parameter count with an efficient MoE architecture to balance performance and computational cost.

Sep 9, 2025
Major releaseApache 2.0
Qwen3-Next-80B-A3B-Instruct

The Qwen team at Alibaba has released Qwen3-Next-80B-A3B-Instruct, a new large language model that employs a Mixture-of-Experts (MoE) architecture. This release marks the introduction of the Qwen3-Next series, signaling a focus on more computationally efficient designs for powerful models.

The key feature of this model is its MoE structure. While it contains a total of 80 billion parameters, only 3 billion are activated for processing any given token. This design aims to provide the knowledge and nuance of a very large model while keeping inference costs significantly lower, making it more accessible for a wider range of applications and hardware setups.

Technical Specifications

Beyond its architecture, Qwen3-Next is an instruction-tuned model designed for chat and task completion. It supports a context length of up to 65,536 tokens, making it suitable for tasks requiring long-form context and analysis. The model is built on a standard Transformer foundation with SwiGLU activations and Group Query Attention for efficiency.

Released under the permissive Apache 2.0 license, the Qwen3-Next-80B-A3B-Instruct model is available for both research and commercial use. This continues the trend of major AI labs contributing powerful, open models that allow developers to build without restrictive licensing, fostering broader innovation in the ecosystem.

Sources

  • Qwen/Qwen3-Next-80B-A3B-Instruct

    Hugging Face

    Visit
OlderLumina-DiMOO: A Diffusion Model for Any-to-Any AIAlpha-VLLM · Any-to-Any · last yearNewerAlibaba's Wan2.2 Adds Control to Open VideoQwen · Alibaba · Text → Video · last year

Get the model

Hugging Face

Specs

Parameters80B · MoE
Active params3B active
Context window262K tokens
Size162.6 GB
PrecisionBF16
ArchitectureQwen3NextForCausalLM
LicenseAPACHE-2.0
Downloads136.8K
Likes1.1K

Can you run it?

Needs a workstation GPU (64 GB) at 4-bit.

  • BF16 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    164.4 GB
  • 8-bit

    H200 141 GB or 2×80 GB · 192 GB Mac Studio

    88 GB
  • 4-bit

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    50.6 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 81.3B parameters must be resident even though only 3B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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