Qwen3.5-4B-GGUF Windows 11

For the fastest local setup of this model, Docker is the best choice.

Make sure to follow the instructions below.

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

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

📊 File Hash: 8bbff87cadf55e46ce8c62f6910b844c — Last update: 2026-06-25 Verify Processor: high single-core performance needed for token latency 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 **Qwen3.5-4B-GGUF** model delivers strong performance for a range of natural language tasks while maintaining a compact footprint. Built with 4B parameters and optimized for the GGUF quantization format, it balances speed and accuracy for both research and production environments. It supports a context window of up to 8192 tokens, enabling detailed reasoning and multi‑step problem solving without sacrificing latency. Benchmarks show the model achieves competitive perplexity scores on standard benchmarks while consuming less than 5 GB of GPU memory during inference. The integrated

below provides a quick comparison with similar open‑source models, highlighting its efficiency and ease of deployment.

Parameters	4 B
Context Length	8192 tokens
Quantization	GGUF
Memory Usage (inference)	<5 GB

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