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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.
