Descriptions:
Alibaba’s Qwen team has released the Qwen 3.5 small model series, and the 9-billion-parameter variant is the standout entry. Fahd Mirza walks through a complete local deployment using vLLM on Ubuntu with an Nvidia RTX 6000 (48GB VRAM), covering installation, model serving, and hands-on testing across four modalities: code generation, multilingual tasks, image understanding, and video analysis.
The benchmark numbers are striking for a 9B model: 81.7 on GPQA Diamond, 83.2 on HMMT, and 70.1 on a math benchmark — results that place it alongside or ahead of models like GPT-class systems and Qwen 3’s own 80B variant on several tasks. In practice, the model loads in roughly 44–45GB of VRAM, defaults to chain-of-thought reasoning, and supports a 262K native context window extendable to 1 million tokens via YaRN scaling. A self-contained HTML Bitcoin mining animation is generated in a single prompt, with visible chain-of-thought planning before output.
Architecturally, the 9B shares the hybrid layout of the 4B model — 32 layers alternating gated Delta Net blocks and gated attention blocks — but with a larger hidden dimension (4096) and feed-forward intermediate size (12,288). The full Qwen 3.5 small series ships under Apache 2.0 with base model weights included, making it viable for fine-tuning. For developers evaluating compact vision-language models for local deployment, this video provides a thorough, reproducible reference point.
📺 Source: Fahd Mirza · Published March 02, 2026
🏷️ Format: Tutorial Demo
