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How to Run Qwen3.5-397B-A17B-NVFP4 Windows 10 with Native FP4 Full Method

How to Run Qwen3.5-397B-A17B-NVFP4 Windows 10 with Native FP4 Full Method



For the fastest local setup of this model, Docker is the best choice.





Refer to the instructions below to proceed.





Then, run the specified Docker command to start the environment.



🗂 Hash: ca7b11012ea696e8e80d230e285899f7Last Updated: 2026-06-26


  • Processor: 6-core 3.5 GHz minimum required
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space:70 GB free space for full FP16 weights storage
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The Qwen3.5-397B-A17B-NVFP4 model represents a major leap in large language model efficiency, combining a 397‑billion parameter architecture with the ultra‑low‑precision NVFP4 data type.

By leveraging NVFP4 quantization, the model achieves a dramatic reduction in memory footprint while preserving near‑full‑precision performance, making it ideal for deployment on consumer‑grade GPUs.

Benchmarks show that the model delivers sub‑50 ms inference latency and a throughput of over 200 tokens per second on standard hardware, outperforming previous 400B‑scale models.

Its training pipeline incorporates a novel mixture‑of‑experts routing scheme that balances load across the A17B accelerator cluster, resulting in stable convergence and robust multilingual capabilities.

The integrated

ModelParametersPrecisionLatency (ms)Throughput (tokens/s)
Qwen3.5-397B-A17B-NVFP4397BNVFP4<50>200
provides a quick comparison with competing models, highlighting parameter count, precision, latency, and throughput in a concise format.

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