Last Updated on January 3, 2026
Introduction
Professionals requiring high-level data security are increasingly moving away from cloud-based assistants. To achieve a true private Microsoft Copilot alternative, users must deploy a Local LLM directly on their own hardware. This strategy is at the core of our comprehensive guide to Private AI for Word, where we explore the move toward 100% data ownership.
As part of our performance testing, we have evaluated GPT-OSS-20B and Microsoft Phi-4, showing their respective speeds and output results for text rewriting.
Watch: Private AI for Word Demo
This demonstration illustrates the integration of local models within Microsoft Word. The video provides a side-by-side performance comparison between GPT-OSS-20B and Microsoft Phi-4.
The video demonstrates how seamless and efficient this process can be. For more creative ideas on using local LLMs in Microsoft Word, please visit the additional demos available on our @GPTLocalhost channel.
Technical Profile: Why GPT-OSS-20B? (Download Size: 12.11 GB)
When choosing a private AI for Word, it is helpful to look at the underlying architecture. Based on the model’s official documentation and our internal benchmarks, GPT-OSS-20B offers several key advantages for professional use:
- Massive 131K Context Window: Documentation shows a generous context window of 131,072 tokens. This allows the model to “read” and summarize entire contracts or multi-chapter reports in a single pass without losing the thread.
- The Power of MoE: This local LLM uses a Mixture-of-Experts design. It contains 20B total parameters but only activates approximately 3.6B parameters per token. This allows it to deliver the intelligence of a massive model with the speed and efficiency of a much smaller one. According to this post, the 20B model runs at >10 tokens/s in full precision, with 14GB RAM/unified memory.
Technical Profile: Why Phi-4? (Download Size: 9.05 GB)
Phi-4 is a strong fit for offline and private Word integrations because it delivers high-quality reasoning without the heavy infrastructure requirements of large cloud models. With a compact architecture optimized through careful data curation and training, Phi-4 offers reliable performance for document drafting, rewriting, and analysis.
- Efficient yet capable: Phi-4 balances model size and reasoning power, making it practical to run on consumer hardware while still performing well on logic-, math-, and language-heavy tasks commonly encountered in Word documents.
- Open and privacy-friendly: As an open-source model with flexible deployment options, Phi-4 can be used entirely offline, avoiding API costs and ensuring sensitive documents never leave your machine.
Grounded Performance: Tested on Mac M1 Max
Our tests were performed on a Mac M1 Max with 64GB of RAM, which is more than sufficient. In our experience, you do not need a server-grade supercomputer to run a world-class local LLM. The Unified Memory in Apple M-series chips (or a dedicated NVIDIA GPU on PC) provides the high bandwidth necessary for GPT-OSS-20B to generate text almost instantaneously. This ensures your private assistant feels just as fast as cloud-based alternatives.
The Local Advantage
Running GPT-OSS-20B or Phi-4 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.