Training Pipelines

Training a modern AI model involves data engineering, distributed computation, and optimization. This sub-article expands Section 2.3 of Hardware & Software.

Data Engineering

• Collection, cleaning, tokenization, and augmentation.
• Deduplication and quality filtering matter as much as volume.

Distributed Training

• Data-parallelism: replicas train on data shards and sync gradients.
• Model-parallelism: layers/weights split across devices (e.g., Megatron-LM, DeepSpeed).
• Pipeline parallelism and tensor parallelism reduce latency and memory.

Optimization

• Stochastic gradient descent variants (Adam, SGD) with learning-rate schedules.
• Mixed-precision training (FP16/BF16) speeds arithmetic.
• Gradient clipping, weight decay, and normalization stabilize training.

Scale and Cost

• Frontier models require thousands of accelerators and weeks of compute.
• Cost drives research into efficiency (distillation, sparse attention, quantization).

Why It Matters

Training infrastructure determines what models can be built and how expensive they are.

Sources

• Rajbhandari, S. et al. (2020). ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.
• Shoeybi, M. et al. (2019). Megatron-LM.

Back to Hardware & Software.