Qwen Qwen3. 5-35B-A3B · Hugging Face In particular, Qwen3 5-Flash is the hosted version corresponding to Qwen3 5-35B-A3B with more production features, e g , 1M context length by default and official built-in tools For more information, please refer to the User Guide
qwen3. 5:35b-a3b Qwen3 5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency
qwen qwen3. 5-35b-a3b • LM Studio Qwen3 5 is a reasoning vision-language model that supports tool use With 35B total parameters and 3B activated, it outperforms previous generation models more than 6x its size
Qwen3. 5-35B-A3B | NVIDIA NGC This container houses the Qwen3 5-35B-A3B model, which is a multimodal vision-language Mixture-of-Experts model designed for native multimodal agent applications, supporting text, image, and video inputs
Qwen3. 5-35B-A3B: Specifications and GPU VRAM Requirements Qwen3 5-35B-A3B is Alibaba Cloud's efficient multimodal foundation model, released February 2026 With 35B total parameters and 3B activated through a Mixture-of-Experts architecture (256 experts), it delivers strong performance with minimal compute
Qwen3. 5 35B-A3B (MoE) - Jetson AI Lab Qwen3 5 35B-A3B is a Mixture-of-Experts (MoE) model from Alibaba Cloud’s Qwen3 5 family It features 35 billion total parameters with only 3 billion active during inference, delivering strong performance with excellent efficiency on edge devices
Qwen3. 5-35B-A3B · Models Qwen3 5 can be served via APIs with popular inference frameworks In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3 5 models
GitHub - QwenLM Qwen3. 6: Qwen3. 6 is the large language model series . . . Building upon the fundamental breakthroughs of Qwen3 5, this release prioritizes stability and real-world utility It offers developers a more intuitive, responsive, and genuinely productive coding experience, shaped by direct community feedback
Qwen3. 5 - How to Run Locally | Unsloth Documentation Between 27B and 35B-A3B, use 27B if you want slightly more accurate results and can't fit in your device Go for 35B-A3B if you want much faster inference If you're getting gibberish, your context length might be set too low Or try using --cache-type-k bf16 --cache-type-v bf16 which might help