Pre-Training LLM Engineering: Distributed Training, Architecture Design, and Scaling Laws

The Engineering Playbook for Pre-Training Foundation ModelsLLM Pre-Training Engineering is a practical guide for ML engineers and AI infrastructure teams who need to train large language models from scratch or continue pre-training open-source checkpoints on domain-specific data.- Build large-scale data pipelines with deduplication, quality filtering, and dataset governance- Design tokenizers for general-purpose and domain-specific vocabularies- Configure modern transformer architecture components for better training efficiency- Plan distributed training with sharding, parallelism, and large-scale optimizer strategies- Monitor training stability and recover from common training instabilities- Apply compute-optimal scaling principles to improve budget and hardware planning- Evaluate pre-training progress with benchmark design and validation workflows