Qwen3-ASR-1.7B on AMD/Nvidia GPU

Qwen3-ASR-1.7B on AMD/Nvidia GPU

The most efficient approach for a local installation is leveraging Docker containers.

Check out the detailed setup guide below to begin.

The setup auto-downloads all needed files (several GBs).

The deployment tool scans your environment and chooses the ideal parameters.

🖹 HASH-SUM: 25f18f4213f1a17dd7d2c044af6f881a | 📅 Updated on: 2026-06-27



  • Processor: high single-core performance needed for token latency
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The Qwen3-ASR-1.7B model delivers high‑accuracy automatic speech recognition across a wide range of languages and accents. Built on an efficient transformer architecture, it balances performance with a modest 1.7 B parameter count, making it suitable for both research and production environments. Its training leverages large‑scale multilingual corpora, enabling real‑time transcription with low latency on consumer hardware. The model incorporates advanced noise‑robustness techniques, ensuring reliable output even in challenging acoustic settings. Below is a quick overview of its core specifications:

Model Name Qwen3-ASR-1.7B
Parameters 1.7 B
Language Support Multilingual ASR
Key Feature Real‑time speech transcription
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