Edge Computing Trend

1. As large-scale AI models like Transformers and CNNs mature, many AI application scenarios are shifting from relying on massive models to optimizing smaller ones. These smaller models are fine-tuned to deliver performance comparable to their larger counterparts.

   For example, an optimized 7B model can match the performance of a 70B model in tasks such as text generation, chatbots, and simple code generation.

   However, training these massive models is a resource-intensive process that requires significant computational power and time, making it costly. In contrast, smaller models like 3B or 7B require less computing power and can run on edge servers or AIPC terminals, making them more accessible in cost-sensitive scenarios.

   As developers continue to leverage techniques such as model distillation, quantization, and pruning to optimize smaller models, their performance and efficiency will improve further. This technological progress will enable AI to be used in an even broader range of scenarios.

2. Advancements in chip manufacturing technology have significantly improved the performance and energy efficiency of edge AI chips. This progress has enabled the deployment of more complex AI models and algorithms on edge devices.

   For example, the Apple MAC Mini 4 and Qualcomm AIPC exhibit impressive INT8 performance, exceeding 50 TOPs (tera-operations per second). This enhanced capability allows these devices to run demanding AI models like LLaMA 7B with remarkable efficiency. The LLaMA 7B model on these devices achieves an impressive generation rate of 30 tokens per second, demonstrating their ability to support complex AI workloads.

3. As AI applications evolve towards multimodal fusion and the development of intelligent agents, edge-integrated AI computing networks will offer even greater advantages. By combining smaller models (7B) deployed on edge servers with more powerful servers (70B), terminals can be equipped with the ability to fuse multiple models and evolve.

   This approach leverages the strengths of edge AI, including high-efficiency response times and low costs, to enable seamless data processing, analysis, and decision-making. The integrated network will provide robust infrastructure for complex applications that require real-time insights, making it an attractive solution across various industries.

   With performance capabilities of up to 50 TFLOPS, ARM-based computing terminals can maintain an overall power consumption of less than 50 watts. In terms of edge service capabilities, their performance-to-power ratio significantly outperforms high-performance GPUs like NVIDIA’s H100 or 4090.

4. Low-cost, low-power edge computing devices can be rapidly deployed worldwide. Distributed networks are optimized based on demand and geography, providing low latency and high utilization rates.