RAM capacity determines how much data you can preprocess before transferring to GPU. 32GB handles small to medium datasets, 64GB supports most professional work, 128GB enables large-scale preprocessing, and 256GB+ is needed for enterprise data pipelines and massive datasets.

Higher DDR5 speeds (5600-6400 MHz) improve data loading and preprocessing performance. Faster RAM reduces bottlenecks when feeding data to GPU. For AI workloads, DDR5-5600 is minimum, DDR5-6000+ recommended for optimal throughput in data-intensive applications.

ECC (Error-Correcting Code) memory detects and corrects data corruption, crucial for long-running training jobs and enterprise workloads. While consumer DDR5 works fine for most tasks, ECC prevents silent data corruption in mission-critical applications and research.

CAS Latency (CL) affects memory response time. Lower CL means faster access. For AI: CL30-CL36 is excellent for consumer RAM, CL46 is standard for ECC modules. While important, capacity and bandwidth matter more than latency for data preprocessing workloads.

Dual-channel (2 modules) or quad-channel (4 modules) configurations maximize bandwidth. For consumer platforms: 2x32GB or 4x32GB. For workstation: 4x64GB or 8x32GB. More modules = more bandwidth for parallel data preprocessing, critical for multi-threaded AI pipelines.