Docker offers the quickest path to setting up this model locally.

Make sure to follow the instructions below.

The loader auto-caches the model archive (several GBs included).

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🖹 HASH-SUM: 77cb1812366d07ef7d6edfae8038f947 | 📅 Updated on: 2026-06-23 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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

1. Script fetching custom model merges directly into KoboldAI directory structures
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