Shireesh Kumar Singh

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GPUs serve as the fundamental computational engines for AI. However, In large-scale training environments, overall performance is not limited by processing speed, but by the speed of the network communication between them. Large language models are trained on thousands of GPUs, which creates a huge amount of cross-GPU traffic. In these systems, even the smallest delays compound. A microsecond lag when GPUs share data can cause a chain reaction that adds hours to the training job.

Communication systems have evolved from simple bit transmission to intelligent information sharing. Traditional systems focus on moving raw data from point A to point B as reliably as possible. Now, with the explosion of machine-to-machine communication in IoT devices, autonomous systems, and smart infrastructure, we’re hitting a fundamental bottleneck. Modern networks are drowning in unnecessary data. But machines don’t need every bit of information that traditional systems transmit.