Last Updated on January 8, 2026
Introduction
In the landscape of Private AI for Word, the ability to transform and polish professional prose without sacrificing data security is a high-priority requirement. For legal, medical, and corporate professionals, moving to a private Microsoft Copilot alternative ensures that sensitive drafts remain 100% offline. This move toward absolute data ownership is at the heart of our Ultimate Guide to Local LLMs for Microsoft Word.
As part of our continuous performance testing, we have evaluated Google’s Gemma-3-27B-IT-QAT. This powerful model is suitable for high-fidelity text rewriting, and it is now possible to access its capabilities directly inside Microsoft Word. By using GPTLocalhost as a Word Add-in, you can enable this model for Word, providing a 100% private drafting experience on your desktop.
Watch: Gemma-3 for Text Rewriting Demo
This demonstration illustrates how the Gemma-3 QAT model handles text rewriting entirely within Microsoft Word.
Another demo shows how Gemma-3 summarizes an article as below.
For more professional use cases of private GPT models within Microsoft Word, please visit our channel at @GPTLocalhost.
Technical Profile: Why Gemma-3-27B-IT-QAT? (Download Size: 16.43 GB)
When selecting a private AI for Word for rewriting tasks, the “QAT” (Quantization-Aware Training) architecture of Gemma-3 offers a significant edge. Based on this post, this model delivers several key advantages:
- Accessibility to Powerful AI on Local Hardware: The main advantage is the ability to run a large, powerful 27-billion-parameter model on a single consumer GPU, such as an NVIDIA RTX 3090 (24GB VRAM). The standard, unquantized version of the model typically requires much more memory (around 54 GB) which is generally only found in expensive, high-end data center GPUs.
- Versatile Capabilities: The base Gemma 3 27B instruction-tuned model (which the QAT version is based on) offers a wide range of state-of-the-art capabilities:
- Long Context Support: It handles a large context window of up to 128,000 tokens, enabling it to process long documents or conversations.
- Multilingual Support: It has out-of-the-box support for over 35 languages and exposure to over 140 languages during pre-training.
Deployment: Grounded Performance on Mac M1 Max
Our tests were performed on a Mac M1 Max (64GB RAM), which is more than sufficient. Thanks to the Unified Memory architecture of Apple Silicon, the Gemma-3-27B-IT-QAT model generates text smoothly. As aforementioned, you can also run the model with NVIDIA RTX 3090 (24GB VRAM).
By leveraging Private AI for Word, you eliminate the risks associated with third-party servers. Your document remains on your local disk, and the “brain” processing your text is running locally on your NPU or GPU. This ensures that your private assistant is not only secure but also faster and more reliable than cloud-based alternatives.
The Local Advantage
Running this Gemma-3 model locally via GPTLocalhost ensures:
- Data Ownership: No cloud data leaks.
- Zero Network Latency: Faster performance on GPU and Apple Silicon.
- Offline Access: Work anywhere, including on a plane ✈️, without an internet connection.