Docker offers the quickest path to setting up this model locally.

Make sure to follow the instructions below.

1-click setup: the app automatically fetches the large weight files.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🔐 Hash sum: fe4bd05df3863f65ba73b81500b318e6 | 📅 Last update: 2026-06-28 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Qwen3.5-9B-NVFP4 is a cutting‑edge language model designed for high performance and efficiency. Built on a 9‑billion parameter foundation, it leverages NVFP4 quantization to deliver faster inference while maintaining strong contextual understanding. Trained on a diverse web‑scale corpus, the model excels in reasoning, coding, and multilingual tasks, offering developers a versatile tool for production environments. Key specifications are shown below:

Its optimized memory footprint and support for FP4 hardware acceleration make it particularly suitable for edge deployments and cloud‑scale services.

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