1 de julho de 2026

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por: cursos

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Categorias: Chunkers

Run Qwen3.5-397B-A17B-NVFP4 PC with NPU with Native FP4 Direct EXE Setup

Run Qwen3.5-397B-A17B-NVFP4 PC with NPU with Native FP4 Direct EXE Setup

Deploying locally takes the least amount of time when executed through native OS tools.

Simply follow the directions outlined below.

The client handles the setup, pulling gigabytes of data automatically.

The installer diagnoses your environment to deploy the most compatible profile.

🔍 Hash-sum: c1a59242c3703fef126d00efd1de319b | 🕓 Last update: 2026-06-28
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  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space:70 GB free space for full FP16 weights storage
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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

Model Parameters Precision Latency (ms) Throughput (tokens/s)
Qwen3.5-397B-A17B-NVFP4 397B NVFP4 <50 >200

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

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