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Qwen3.5-397B-A17B-NVFP4 Locally (No Cloud) with 1M Context

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Qwen3.5-397B-A17B-NVFP4 Locally (No Cloud) with 1M Context

Qwen3.5-397B-A17B-NVFP4 Locally (No Cloud) with 1M Context

The fastest way to get this model running locally is via Docker.

Refer to the instructions below to proceed.

Then, execute the docker-compose up command to launch the model.

💾 File hash: 86eb01786dab53d2db3eb66ce93e08b8 (Update date: 2026-06-21)
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  • Processor: 6-core 3.5 GHz minimum required
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk: 150+ GB for high-context vector database storage
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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