Last Updated on January 8, 2026
Introduction
Private AI for Word is setting a new benchmark for technical professionals who require rigorous analytical depth without the privacy compromises of cloud-based LLMs. To achieve a true private Microsoft Copilot alternative for math, users are shifting toward reasoning models. These models are not just predicting text; they are designed to “think” through logic-heavy problems. This local-first approach is the foundation of our comprehensive guide to Private AI for Word, where we prioritize 100% data ownership.
As part of our evaluation of local LLMs for GPTLocalhost, we have tested the DeepSeek-R1 models. Trained via large-scale reinforcement learning, DeepSeek-R1 demonstrates reasoning capabilities that rival the world’s most advanced proprietary models, bringing elite-level mathematical problem-solving directly to your desktop. The models have outperformed OpenAI-o1 and Claude3.5 Sonnet on various benchmarks, according to Mehul’s post and Tony’s post.
Watch: DeepSeek-R1 Math Reasoning Demo
This demonstration shows the integration of DeepSeek-R1 within Microsoft Word. The video showcases the model solving complex, multi-step mathematical equations and generating a visible “Chain-of-Thought” reasoning process before delivering the final result—all within the familiar Word interface.
Our demo highlights how seamless local inference can be for solving math equations. For more ideas on using private GPT models in Microsoft Word, please visit the additional demos available on our @GPTLocalhost channel.
Technical Profile: Why DeepSeek-R1? (Download Size: 18.44 GB)
DeepSeek-R1 is a first-generation reasoning model that uses Group Relative Policy Optimization (GRPO) to incentivize self-verification and reflection. For local users, the “distilled” versions (7B to 70B) offer a perfect balance of performance and accessibility.
- Autonomous Reasoning (CoT): DeepSeek-R1 utilizes
<think>tags to break down problems. It doesn’t just give an answer; it verifies its own steps, identifies errors, and self-corrects in real-time. - Distilled Efficiency: By distilling the reasoning patterns of the massive 671B flagship into smaller models (like the Qwen and Llama-based models), DeepSeek-R1 provides high-tier logic that runs smoothly on consumer GPUs.
Deployment Reminders: Hardware & VRAM
Our primary testing was conducted on an Apple Silicon Mac (M1 Max, 64 GB). Because DeepSeek-R1 comes in various sizes, you can “right-size” the model to your hardware:
- Distilled 7B/8B: Requires ~8GB VRAM. Ideal for standard laptops and mid-range PCs.
- Distilled 14B/32B: Requires 12GB to 24GB VRAM. Recommended for workstations or high-end gaming GPUs (RTX 3090/4090).
- The Full 671B (MoE): Requires a multi-GPU setup or massive System RAM (192GB+).
For most Microsoft Word users, the DeepSeek-R1-Distill-Qwen-14B offers the “sweet spot” for complex math without requiring enterprise-grade hardware. For more details, please refer to this post.
The Local Advantage
Running DeepSeek-R1 locally via GPTLocalhost ensures:
- Data Ownership: No cloud data leaks; your formulas and proprietary data stay offline.
- Zero Network Latency: Faster performance on GPU and Apple Silicon by eliminating server wait times.
- Offline Access: Solve complex problems anywhere, including on a plane ✈️, without an internet connection.