Challenges and Optimization Techniques for Large Language Model Training

Before discussing optimization tricks for large language model (LLM) training, the article first highlights the key challenges: as model parameters grow, resource consumption and efficiency become bottlenecks, Moore's law only doubles compute every 18‑24 months, and storage bandwidth lags behind, forcing multi‑node distributed training.

The training pipeline must split both data and model parameters, leading to four parallelism techniques—data parallelism, model parallelism, pipeline parallelism, and tensor parallelism—each aiming to increase parameter scale or improve resource utilization.

For I/O optimization, the article recommends high‑availability large‑capacity storage combined with local caching or distributed caching, using prefetching, pin memory, and P2P chain distribution to reduce latency and achieve high throughput for multi‑TB training datasets.

Communication optimization focuses on leveraging fast intra‑node links (NVLink, 300 Gbps) and inter‑node networks (100‑200 Gbps Ethernet, RDMA up to 800 Gbps) to minimize synchronization overhead, with preferred strategies such as hybrid (3‑D) parallelism, ZeRO‑3, and read‑through/read‑neighbour caching.

Stability optimization addresses the long training cycles of LLMs by recommending proactive health monitoring, fault detection, and rapid recovery procedures, including regular checkpointing (≈2 hours), redundancy in storage, and configuring network parameters (e.g., RoCE, PFC) to ensure lossless data transfer.

The content is excerpted from the 2024 book "Large Language Models: Principles and Engineering Practice," which provides detailed implementation guidance and code resources for practitioners.