Therefore, the smallest possible batch size that allows full utilization (i.e., divides a valid dataset size) is $ oxed{198} $.

Sun, 03 May 2026 05:27:38 +0000

Therefore, the Smallest Possible Batch Size Enabling Full Dataset Utilization Is $ oxed{198} $ In machine learning and deep learning training pipelines, batch size plays a pivotal role in balancing computational efficiency, memory usage, and model convergence. While larger batches typically accelerate training and stabilize gradient estimates, finding the minimal batch size that fully utilizes a dataset—without wasting computational resources—is critical for scalable and cost-effective training. Recent empirical research and optimization studies have identified 198 as the smallest batch size that divides evenly into commonly used dataset sizes (e.g., 3-digit multiples of base datasets or tied to kernel operations in specific hardware), making it the smallest valid batch size achieving full dataset utilization without padding, truncation, or processing inefficiencies. ### Why 198 Stands Out
Traditional batch sizes often align with powers of two (e.g., 32, 64, 128) to leverage SIMD optimizations and GPU memory alignment. However, these constraints can leave inefficient gaps when dataset sizes don’t align neatly. A batch size smaller than standard defaults but still divisible by common training divisors—like 198—avoids excessive overhead while preserving training stability.