Gradient Compression Distributed Training is a technique reducing communication volume during distributed training by compressing gradient updates before transmission, minimizing network bottlenecks — Gradient compression addresses the fundamental bottleneck that communication costs often dominate computation in distributed training, especially with many small models or limited bandwidth. Quantization Techniques reduce gradient precision from FP32 to INT8 or lower, reducing transmission size 4-32x while maintaining convergence through careful rounding and stochastic quantization. Sparsification transmits only gradients exceeding magnitude thresholds, reducing transmission volume 100x while preserving convergence through momentum accumulation. Low-Rank Compression approximates gradient matrices with low-rank decompositions, exploiting correlations between gradient components. Layered Compression applies different compression ratios to different layers based on sensitivity analysis, aggressively compressing insensitive layers while preserving precision in sensitive layers. Error Feedback accumulates rounding errors between iterations, compressing accumulated errors rather than original gradients maintaining convergence. Adaptive Compression varies compression ratios during training, compressing aggressively early in training when noise tolerance is high, reducing compression as training converges. Communication Hiding overlaps gradient communication with backward computation and weight updates, hiding compression and transmission latency. Gradient Compression Distributed Training enables distributed training on bandwidth-limited systems.