Last Updated on February 26, 2026
If you’re seeking private LLMs for Microsoft Word, consider the latest Skywork-OR1 series models. This series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement. The 7B model exhibits competitive performance compared to similarly sized models in both math and coding scenarios. With LocPilot in Word, you can seamlessly run the Skywork-OR1-8B model directly within Microsoft Word. Host it locally to unlock powerful LLM functionalities while maintaining complete privacy without any monthly fees. This direction is at the core of our Local LLM Benchmarks for Microsoft Word, where we explore the move toward 100% data security on your intranet.
For a quick demonstration, watch our brief demo video. The demo is powered by GPTLocalhost, which offers the same core features for individual use. LocPilot in Word is the Intranet edition of GPTLocalhost designed for enterprise users and team collaboration.
For more creative uses of local and private LLMs in Microsoft Word, explore additional demos available on our channel at @LocPilot.
Technical Profile: Why Skywork-OR1? (Download Size: 4.68 GB)
Selecting a Private AI for Word involves matching the model’s specialized intelligence to your requirements. For tasks that demand rigorous analytical depth, consider the latest Skywork-OR1 series models. This series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement. The 7B model exhibits competitive performance compared to similarly sized models in both math and coding scenarios.
- Rule-Based Reinforcement Learning: Unlike standard models, Skywork-OR1 was trained using large-scale reinforcement learning (RL). This allows it to verify its own steps during a “Chain-of-Thought” (CoT) process, drastically reducing hallucinations in math and logic.
- Math & Code Specialization: Skywork-OR1-32B (the larger variant) has demonstrated performance parity with frontier models like DeepSeek-R1 on math tasks. The 7B version provides a high-efficiency balance, offering deep reasoning that fits on standard consumer hardware.
Deployment Reminders: Running Skywork-OR1 Locally
Our primary testing was conducted on an M1 Max (64 GB), which is more than sufficient. The Skywork-OR1 models are highly efficient across a wide range of setups. If you plan to deploy Skywork-OR1, please keep these community-recommended hardware considerations in mind:
- According to this Llamacpp imatrix Quantizations guide, the first thing to figure out is how big a model you can run. To do this, you’ll need to figure out how much RAM and/or VRAM you have.
- If you want your model running as fast as possible, you’ll want to fit the whole thing on your GPU’s VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU’s total VRAM.
- If you want the absolute maximum quality, add both your system RAM and your GPU’s VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
The Local Advantage
Running your LLM models locally via LocPilot ensures:
- Air-Gapped Security: Operate entirely within your intranet — no external connections.
- Cost Savings: Eliminate subscription fees for the entire team — no ongoing costs.
- Model Flexibility: Easily host and switch models to suit your use cases — no vendor lock-in.