How long does it take to train an LLM?

Training a large language model (LLM) typically takes weeks to months, depending on factors like model size, hardware resources, dataset scale, and optimization strategies. For example, training a smaller model like GPT-2 Small (117 million parameters) might take a few days on a single high-end GPU cluster, while a massive model like GPT-3 (175 billion parameters) could require months of distributed training across thousands of GPUs. The process involves iterating over vast datasets, adjusting model weights through backpropagation, and tuning hyperparameters like learning rates. The time investment scales nonlinearly with model size, as larger architectures demand more computational steps and memory management.

Three primary factors influence training duration: computational resources, dataset size, and architectural choices. A model with billions of parameters needs specialized hardware (e.g., NVIDIA A100 or H100 GPUs, TPU pods) to handle matrix operations efficiently. Distributed training frameworks like PyTorch’s FSDP or TensorFlow’s Mesh can parallelize workloads, but communication overhead between devices adds complexity. Dataset preprocessing also impacts timelines—training on a 1-terabyte text corpus might take weeks just to process and tokenize before model training begins. For instance, Meta’s LLaMA 2 (70B parameters) reportedly required over 1.7 million GPU hours, illustrating how resource-intensive even optimized setups can be.

Practical optimizations can reduce training time. Techniques like mixed-precision training (using 16-bit floats instead of 32-bit) speed up computations, while model parallelism splits layers across devices to avoid memory bottlenecks. However, these optimizations require careful implementation to avoid instability or accuracy loss. Startups or smaller teams often use pre-trained base models and fine-tune them for specific tasks (e.g., adapting BERT for legal documents), which might take hours instead of months. Ultimately, the timeline depends on trade-offs between cost, hardware availability, and project goals—training from scratch is rarely practical without substantial infrastructure.