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While the industry is racing on port speed, AI compute network have already entered the System Era.
RDMA (Remote Direct Memory Access) is a high-speed network interconnection technology. Its core capability is to enable direct cross-device memory access while bypassing the CPU and operating system kernel, which significantly reduces communication latency and CPU consumption.
The explosive growth of AI large models and general computing power is driving the rapid upgrade of data center interconnection bandwidth from 800G to 1.6T, 3.2T and even 6.4T.
Who can build a more efficient computing network? Who can make a GPU truly "run at full capacity"?
In AI training scenarios, PFC provides lossless transmission guarantees for RoCEv2/RDMA through buffer watermarks and queue scheduling, while ECN enables fast end-to-end congestion detection and mitigation, working in conjunction with intelligent scheduling to optimize link load.
Against the backdrop of the rapid development of distributed AI training, the contradiction between computing power improvement and communication efficiency has become increasingly prominent.
Training modern large language models no longer involves a handful of servers. Today's AI clusters may contain hundreds or thousands of GPUs working together across an Ethernet fabric, exchanging massive volumes of data throughout the training process.
AI training teaches models using massive GPU clusters demanding high bandwidth and lossless networks; inference applies those skills to deliver fast predictions, requiring low latency and flexible deployment. Both stages place distinct demands on network infrastructure.
