The fastest way to get this model running locally is via Docker.
Just follow the guidelines provided below.
The installer auto-downloads and deploys the entire model pack.
During setup, the script automatically determines and applies the best settings tailored to your machine.
🗂 Hash: 23416891f338d648af32f0047ab26dcb • Last Updated: 2026-06-22
CPU: modern architecture (Zen 3 / Alder Lake minimum)
RAM: fast 5600MHz+ required to avoid memory bottlenecks
Disk Space: required: fast PCIe 4.0 drive for instant boots
Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration
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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