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LatestQwen · Alibaba3.6-35B-A3B
Qwen · AlibabaVision-Language

Qwen Releases 35B Multimodal Mixture-of-Experts Model

The new Qwen3.6-35B-A3B from Alibaba's Qwen team combines vision and language capabilities using an efficient sparse architecture.

Apr 15, 2026
Major releaseApache 2.0
Qwen3.8-27B

Alibaba's Qwen team has released a new large multimodal model, Qwen3.6-35B-A3B, adding a sophisticated new option to the open-source AI landscape. As a Vision Language Model (VLM), it is designed to understand and reason about both text and images, making it suitable for a wide range of complex tasks.

The model's key feature is its Mixture-of-Experts (MoE) architecture. While it contains a total of 35 billion parameters, it only activates approximately 3 billion of them for any given input. This sparse activation approach allows the model to achieve the performance of a much larger dense model while requiring significantly less computational power during inference, a crucial advantage for practical deployment.

Why it matters

Qwen3.6-35B-A3B represents a significant contribution to the growing field of efficient, high-performance open models. By combining a multimodal design with a sparse MoE architecture, it provides researchers and developers with a powerful tool that balances capability with computational cost.

The model has been released on Hugging Face under a permissive Apache 2.0 license, encouraging broad adoption and further innovation within the community. Its release continues the trend of major AI labs making increasingly powerful foundation models accessible to the public.

Sources

  • Qwen/Qwen3.6-35B-A3B

    Hugging Face

    Visit
  • unsloth/Qwen3.6-35B-A3B-MTP-GGUF

    Hugging Face

    Visit
OlderMotif Releases 2B Open-Source Text-to-Video ModelMotif Technologies · Text → Video · 7 months agoNewerIBM Releases 2B Granite Model for Multilingual SpeechIBM · Speech → Text · 7 months ago

Get the model

Hugging FaceHugging Face

Specs

Parameters35B · MoE
Active params3B active
Context window262K tokens
Size71.9 GB
PrecisionBF16
ArchitectureQwen3_5MoeForConditionalGeneration
LicenseAPACHE-2.0
Downloads4.6M
Likes3.1K

Can you run it?

Runs on a 24 GB GPU at 4-bit.

  • BF16 (as published)

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

    73.6 GB
  • 8-bit

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    39.8 GB
  • 4-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    23.3 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 36B 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

Vision-LanguageText / LLMReasoning
10 versions — view changelog

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