How to Run Qwen3.5-9B-NVFP4 with Native FP4 Windows

June 30, 2026

How to Run Qwen3.5-9B-NVFP4 with Native FP4 Windows

Running this model locally is fastest when deployed through a PowerShell script.

Please follow the instructions listed below to get started.

The download manager will automatically pull several gigabytes of data.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

📊 File Hash: 7dd97dcacdbe08ae736fe905ca910d56 — Last update: 2026-06-23
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  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Storage: extra room for future model updates and datasets
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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:

Parameters 9 B
Quantization NVFP4
Context Length 8K tokens
Training Data Web‑scale corpus

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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