Ron explains the efficiency of tensor parallelism, achieving only 5-7% overhead by overlapping communication and computation. When models exceed memory capacity, like those with 200 billion parameters, pipeline parallelism becomes essential, allowing for the distribution of layers across multiple devices while managing weights and gradients. This approach addresses the challenges of training massive models effectively.
