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Qwen3

Alibaba's open-weight Qwen3 language models, dense and mixture-of-experts, from 0.6B to 235B-A22B.

Open-weight Qwen3 language models

Apache 2.0 weights you download and serve yourself, with 256K-token context in the 2507 releases.

Free forever · open source · Apache-2.0 (model weights, per README; repo has no LICENSE file)
✓ Needs IT✓ Browser-based✓ Real-time collaboration
6.4
ZEKAI
Self-hostable Apache-2.0 LLMs
Free & open source · Software
Ease of setup3
Usefulness8
Maintenance5.5
Community9.2
★ 27,729
GITHUB
Jan 2026
UPDATED
Apache-2.0 (model weights, per README; repo has no LICENSE file)
LICENSE
🛠 Don't want to self-host? We can set it up →
⚡ Quick answer

This repository documents Qwen3, the open-weight language model family from the Qwen team. It suits developers and researchers who want to run capable models on their own hardware.

Runs on
Cross-platform
Hosting
Self-host
Languages
Python
License
Apache-2.0 (model weights, per README; repo has no LICENSE file)

What it is

The README lists dense models at 0.6B, 1.7B, 4B, 8B, 14B and 32B plus mixture-of-experts 30B-A3B and 235B-A22B. The 2507 updates add Instruct and Thinking variants with 256K-token context, extendable to 1 million tokens. Docs cover llama.cpp, Ollama, LM Studio, vLLM, SGLang and TGI.

Who it's for

It suits teams that can supply GPUs or a capable machine and want no per-token bill. It does not suit anyone expecting a one-click app: you download weights from Hugging Face or ModelScope and pick a serving stack.

Key features

Dense and MoE sizes

Models from 0.6B up to 235B-A22B.

2507 Instruct and Thinking

Updated variants in 235B-A22B, 30B-A3B and 4B.

Long context

256K-token context, extendable to 1 million tokens.

Local run guides

Docs for llama.cpp, Ollama and LM Studio.

Deployment guides

Docs for SGLang, vLLM and TGI.

Quantization docs

GPTQ, AWQ and GGUF guidance.

How to start

Up and running in 3 steps

1
Pick a model from the Qwen3 collection on Hugging Face or ModelScope.
2
Choose a runtime from the docs: llama.cpp, Ollama or LM Studio locally, or vLLM or SGLang for serving.
3
Load the weights and query the model through the runtime's API.
Real talk

Our honest verdict

What's great

  • Weights are under Apache 2.0, which the README states for all open-weight models.
  • Small sizes run on modest hardware, big sizes scale up.
  • Works with the common local runtimes.
  • Long-context variants are available.

Where it falls short

  • The repository has no LICENSE file at its root; licences are in each Hugging Face repo.
  • Larger models need serious GPU memory.
  • You manage serving, updates and safety filtering yourself.
Cost

Pricing & license

The weights are free under Apache 2.0. Alibaba also sells hosted Qwen access separately; the repo shows no price, so your cost is the hardware you run it on.

Paid work

Open remote AI-training roles

870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.

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Questions

FAQ

Can I use Qwen3 commercially?

The README says all open-weight models are licensed under Apache 2.0; check the licence file in each Hugging Face repo.

Which sizes exist?

0.6B, 1.7B, 4B, 8B, 14B, 32B, 30B-A3B and 235B-A22B.

Can I run it locally?

Yes, the docs cover llama.cpp, Ollama and LM Studio.

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