Last Updated on January 8, 2026
Introduction
Private AI for Word is becoming the primary solution for professionals requiring high-level data security who 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 to eliminate the risks associated with third-party data processing. This focus on privacy is the focus of our Ultimate Guide to Local LLMs for Microsoft Word.
As part of our evaluation of local LLMs for Word users, we have tested DeepHermes-3-Llama-3-8B-Preview. This model is the latest version of the flagship Hermes series of LLMs by Nous Research, and one of the first models in the world to unify Reasoning (long chains of thought that improve answer accuracy) and normal LLM response modes into one model.
Watch: DeepHermes-3 Summarization Demo
This demonstration highlights how DeepHermes-3 solves a math equation. Watch as the model utilizes its “Chain-of-Thought” reasoning to analyze a the equation and generate a structured, high-fidelity solution directly inside Microsoft Word without any data leaving the local machine.
Our demo illustrates how seamless local inference can run on edge devices. For more creative ideas on using private GPT models in Microsoft Word, please visit the additional demos available on our @GPTLocalhost channel.
Technical Profile: Why DeepHermes-3? (Download Size: 4.66 GB)
DeepHermes-3 is a 8-billion parameter model built upon the Llama-3-8B base and further fine-tuned. It is a landmark model that allows users to leverage deep analytical thinking for writing tasks.
- Unified Reasoning & Intuition: Based on standard 8B models, DeepHermes-3 is designed to “think” before it speaks. It can toggle long chains of thought to improve the accuracy of its summaries, ensuring that key nuances in your document aren’t missed.
- Massive Instruction Following: Trained on a vast, diverse dataset (OpenHermes), it excels at following complex summarization prompts.
- High Efficiency for Consumer Hardware: Despite its reasoning depth, its 8B size makes it incredibly fast on modern hardware.
Deployment Reminders: Check VRAM Size
Our primary testing was conducted on an Apple Silicon Mac (M1 Max, 64G), which is more than sufficient. Due to its efficient 8B architecture, DeepHermes-3 can run smoothly on most consumer-grade machines equipped with a GPU or Apple Silicon.
- VRAM Requirements: 8GB of VRAM is typically sufficient to run high-quality quantized versions (like Q8_0 or Q6_K) at high speeds.
- Quantization: If you are working with limited memory, 4-bit or 5-bit quantized variants offer a practical alternative while maintaining impressive performance.
The Local Advantage
Running DeepHermes-3 locally via GPTLocalhost ensures:
- Data Ownership: No cloud data leaks; your sensitive documents stay on your disk.
- Zero Network Latency: Faster performance on GPU and Apple Silicon than many cloud APIs.
- Offline Access: Work anywhere, including on a plane ✈️, without an internet connection.