Running a local LLM alongside Home Assistant on a NAS appears promising for smart home automation. The NAS is already active throughout the day, allowing smart home data to remain within the network and reducing reliance on cloud models. However, most NAS hardware is designed primarily for storage, which means it lacks the computational power necessary to run local LLMs effectively.

The Ugreen DH4300 Plus, equipped with a Rockchip RK3588C and eight ARM CPU cores, was used to test seven different models. These included the FunctionGemma 270M, Qwen3 1.7B, Qwen3 0.6B, and others, ranging in size from 270M to 3.8B parameters. Despite the hardware's capabilities, most models struggled with performance.

The testing revealed that the Ugreen DH4300 Plus, while capable of handling some models, faced limitations with larger LLMs. The Rockchip NPU and 8GB of memory were insufficient for models exceeding certain parameter thresholds, leading to instability and poor performance in most cases. Only one model, among the seven tested, functioned reliably without significant issues.

The results highlight the challenges of deploying LLMs on NAS devices, which are not optimized for such tasks. Users may face increased costs due to the need for more powerful hardware, potential vendor lock-in with specific models, and governance concerns around data handling and model performance. Market reaction suggests that current NAS hardware may not be ready for widespread LLM deployment.

The findings indicate that while the concept of running LLMs on a NAS is appealing, the reality is more complex. Users must weigh the benefits of local processing against the limitations of current hardware. As the technology evolves, improvements in NAS hardware and model optimization may address these challenges, but for now, the performance gap remains significant.