Core Insights - The development of large models in AI is entering a critical phase, with key considerations around user experience, cost, and privacy becoming increasingly important [1] - Deploying large models on the edge (end devices) presents significant advantages, including enhanced privacy, reduced latency, and lower operational costs compared to cloud-based solutions [3][4] - The integration of large models into operating systems is anticipated, as their role in end devices and smart hardware becomes more significant [8] Edge Large Model Deployment - Edge large models refer to running large models directly on end devices, contrasting with mainstream models that operate on cloud-based GPU clusters [2] - The definition of a large model is subjective, but generally includes models with over 100 million parameters that can handle multiple tasks with minimal fine-tuning [2] Advantages of Edge Deployment - Privacy is a major advantage, as edge models can utilize data generated on the device without sending it to the cloud [3] - Edge inference eliminates network dependency, improving availability and reducing latency associated with cloud serving [3] - From a business perspective, distributing computation to user devices can lower the costs associated with maintaining large GPU clusters [3] Challenges in Edge Deployment - Memory limitations on devices (typically 8-12GB) pose a significant challenge for deploying large models, which require substantial memory for inference [4][9] - Precision alignment is necessary as edge models often need to be quantized to lower bit representations, which can lead to discrepancies in performance [5] - Development costs are higher for edge models, as they often require custom optimizations and adaptations compared to cloud deployments [5] Solutions and Tools - Huawei's CANN toolchain offers solutions for deploying AI models on edge devices, including low-bit quantization algorithms and custom operator capabilities [6] - The toolchain supports various mainstream open-source models and aims to enhance the efficiency of cross-platform deployment [6][20] Future Trends - The future of edge AI is expected to evolve towards more integrated systems where large models become system-level services within operating systems [8] - The collaboration between edge and cloud AI is seen as essential, with edge AI focusing on privacy and responsiveness while cloud AI leverages large data and computational power [23][24] - The emergence of AI agents that can operate independently on devices is anticipated, requiring significant local computational capabilities [23][24] Commercialization and Applications - The commercial viability of edge large models is being explored, with applications in various sectors such as personal assistants and IoT devices [21][22] - Companies are focusing on optimizing existing devices for better inference capabilities while also developing new applications that leverage edge AI [22][30]