# Replacing Grammarly Pro with Local LLMs for Private Proofreading How to run local AI models on your GPU to eliminate subscription costs and data privacy risks. - **Source:** [XDA Developers](https://www.xda-developers.com) - **Published:** 2026-11-06 - **Topic:** [technology](https://brifing.si/c/technology) - **Original article:** - **On Brifing:** https://brifing.si/s/3923a171-69f1-4020-bf9d-48389ceea724 ## The Subscription Fatigue Reality Many users are trapped in a cycle of monthly subscriptions for productivity tools like Grammarly Pro. These services often require data to be sent to external servers, raising significant privacy concerns. By transitioning to local models, users can reclaim their data sovereignty while simultaneously eliminating recurring monthly expenses from their bank statements. ## Privacy Risks in Default Settings Grammarly users are often opted into product improvement programs by default, allowing the company to sample de-identified writing excerpts for model training. Even with privacy settings adjusted, the necessity of an internet connection means drafts must leave the user's device. This creates a potential security vulnerability for sensitive personal or corporate communications. ## Overcoming Local Model Accessibility Local models have historically struggled with usability because they were confined to static chat windows. Unlike Grammarly, which integrates into almost every application, local alternatives required manual text copying. The open-source tool Writing Tools solves this by enabling system-wide text selection and processing, effectively bridging the gap between local power and convenience. ## Hardware and Software Integration The setup utilizes an RTX 4070 Ti Super GPU to run a Qwen3.6-35B-A3B model via llama-server. By configuring Writing Tools to communicate with this local instance, every request remains within the machine. This architecture ensures that no third-party server ever accesses the user's drafts, maintaining complete privacy throughout the entire editing process. ## Seamless Workflow Implementation Users can trigger the proofreading tool by pressing Ctrl+Space on selected text. The application offers various presets, including error correction, tone adjustment, and length modification. If the model's output is unsatisfactory, a standard Ctrl+Z command reverts the text to its original state, providing a familiar and efficient user experience for daily writing tasks. ## Performance and Reliability Testing In a controlled test involving a messy email with 24 intentional errors, the local model successfully identified and corrected every mistake. While the tool proved highly reliable for basic proofreading, advanced tone-shifting presets sometimes required human oversight to preserve original meaning. This demonstrates that local models are effective but still require user verification. ## Alternative Deployment Methods While the author utilized llama.cpp for its robust performance, Writing Tools supports multiple deployment paths. Users can leverage Ollama or LM Studio to achieve similar results with significantly less command-line configuration. These options provide accessible entry points for individuals who want to maintain privacy without needing advanced technical expertise in model deployment. ## The Future of Local Productivity Transitioning to local AI models represents a shift toward more private and cost-effective computing. By leveraging existing GPU hardware, users can replicate the functionality of expensive subscription services while ensuring their data never leaves their local machine. This approach offers a sustainable model for privacy-conscious professionals seeking to optimize their digital workflows. ## Sources covering this story - https://www.xda-developers.com/i-replaced-grammarly-premium-with-a-local-model-on-my-gpu-and-my-drafts-never-leave-my-pc/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._