Running this model locally is fastest when deployed through Docker.

Refer to the instructions below to proceed.

Just look at the steps below to finish the installation.

🛠 Hash code: 90a938c6a44b6bf928c781742b3d93e4 — Last modification: 2026-06-23 Verify Processor: next-gen chip for heavy context processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 100 GB for multi-modal model vision components GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Qwen3.5-397B-A17B-FP8 is a state‑of‑the‑art large language model designed for high‑performance inference on modern hardware. It leverages a 397‑billion parameter architecture built on the A17B design, delivering superior reasoning and multilingual capabilities. The model employs FP8 quantization, which reduces memory footprint while preserving accuracy and enabling faster computations. Its extensive training on diverse datasets allows it to generate coherent text, code, and creative content across multiple domains. A concise overview of its key specifications is provided below, highlighting parameter count, context window, and precision for easy reference.

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