Imagine a world where crowd management is seamless, privacy-preserving, and incredibly accurate, even when people are standing still. That's the promise of LLMCount, a groundbreaking system that leverages the power of large language models (LLMs) to enhance millimeter wave (mmWave) sensing for stationary crowd detection. Traditional methods for crowd analysis, like cameras or WiFi, suffer from limitations such as privacy concerns and low accuracy, especially in scenarios with minimal movement. Stationary crowds are notoriously difficult to detect with mmWave as subtle motions like breathing are easily mistaken for noise. LLMCount tackles these challenges head-on by using the unique decision-making capabilities of LLMs. It intelligently compensates for signal power attenuation and filters out noise, resulting in a more uniform and accurate detection. The secret sauce lies in LLMCount's multimodal approach. It combines raw mmWave data with sensor setup parameters and scenario descriptions, all encoded into tokens that the LLM can understand. This contextual information enables the LLM to make smarter decisions about what constitutes noise versus valid signal data, dramatically improving accuracy. Furthermore, LLMCount dynamically compensates for signal power loss due to distance and other environmental factors, ensuring reliable performance across various real-world settings. Through a combination of innovative data processing techniques and cloud-based computation, LLMCount achieves higher accuracy and lower latency compared to existing solutions. Tests in diverse real-world environments like movie theaters, classrooms, and meeting rooms have demonstrated its ability to accurately count people even in challenging, stationary scenarios. LLMCount opens up exciting possibilities for various applications, from optimizing building occupancy and managing public spaces to creating more immersive entertainment experiences. By harnessing the power of LLMs, this technology paves the way for a future of smarter, more efficient, and privacy-respecting crowd management.