Last Updated on February 26, 2026
Looking for an alternative to Microsoft Copilot in Word? Explore the newly released Granite 3.3 and Phi-4-Reasoning series models. Granite 3.3 models feature enhanced reasoning capabilities, support for a 128K context length, and controls for response length and originality. Granite 3.3 also delivers competitive results across general, enterprise, and safety benchmarks, while Phi-4 highlights how small language models can achieve remarkable breakthroughs in AI capabilities. For math, this demo video showcases the local inference of granite-3.3-8b-instruct and phi-4-mini-reasoning.
With LocPilot in Word, you can seamlessly integrate powerful models into your Microsoft Word experience. By hosting these models directly on your own computer, you can ensure complete data privacy and avoid monthly subscription fees while accessing advanced LLM features. 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.
Watch our demo video to see how simple and efficient it is in practice. 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 Granite-3.3-8B–Instruct? (Download Size: 4.94 GB)
According to IBM’s model card, Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. It supports structured reasoning through and tags, providing clear separation between internal thoughts and final outputs. The model has been trained on a carefully balanced combination of permissively licensed data and curated synthetic tasks.
- Multilingual & Versatile Utility: The model supports 12 major languages—including English, Arabic, and Japanese—and excels in a wide array of tasks such as RAG, function-calling, and code generation, with the flexibility to be fine-tuned for additional languages.
- Enhanced Reasoning Performance: Built on top of Granite-3.3-8B-Base, the model delivers significant gains on benchmarks for measuring generic performance and improvements in mathematics. This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
Technical Profile: Why Phi-4-Mini-Reasoning? (Download Size:2.49 GB)
Phi-4-Mini-Reasoning is a lightweight open model that balances efficiency with advanced reasoning ability. Specifically engineered for memory-constrained environments and latency-bound scenarios, this model excels at multi-step mathematical problem-solving and symbolic computation. According to this graph, despite its compact size, it demonstrates remarkable breakthroughs in deep analytical thinking, outperforming many models more than twice its size on benchmarks like GPQA Diamond and Math-500, making it an ideal choice for high-speed, local reasoning within Microsoft Word.
- Logic-Intensive Problem Solving: Designed for formal proofs and symbolic computation, the model balances high-quality reasoning with cost-effective deployment. Its efficient architecture makes it perfectly suited for educational applications, embedded tutoring, and lightweight edge systems where multi-step problem solving is required.
- Large Context and Robust Alignment: Supporting a 128K token context length, the model can process and reason over extensive mathematical proofs and long-form documents. It leverages advanced fine-tuning techniques on high-quality synthetic math datasets, ensuring reliable and robust performance for complex use cases.
Deployment Reminders: Check VRAM Size
These two models were evaluated on a Mac M1 Max. Due to their relatively small download sizes, these reasoning-focused models can generally run smoothly on consumer-grade machines equipped with a GPU or Apple Silicon. For the two models tested, 8GB of VRAM or more should be sufficient. If a model exceeds your available VRAM, low-bit quantized variants offer a practical alternative.
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.