Abstract:

The rapid development of artificial intelligence （AI） large models has posed new challenges to high-performance network architectures. This paper systematically analyzes three core issues： the conflict between high-bandwidth demands and costs，bottlenecks in low-latency stability，and complexities in topology adaptation. It reveals the critical roles of enhancements in remote direct memory access （RDMA） technology，improvements in edge-side capabilities，and end-to-end network collaborative optimization. The study finds that there is an urgent need for industry-academia-research collaboration to address the challenges faced by high-performance network，and provides suggestions for development directions. Future trends point to collaborative innovation in protocol openness，hardware optoelectronic integration，and paradigm autonomy，which breaks the limitations of single-dimensional optimization through cross-layer design，thereby providing high-performance，low-energy-consumption interconnection support for ultra-large-scale AI clusters.

Key words: high-performance network, RDMA, AI