Part 5/8:
Training large language models requires staggering computational resources. To understand the scale, consider that performing a billion additions and multiplications per second would still take over 100 million years to complete all computations involved in training the largest models. This extraordinary feat is achievable only with specialized hardware, such as GPUs, optimized for parallel computing.
Historically, language models processed data sequentially—one word at a time—until 2017, when Google introduced the transformer model. This revolutionary architecture allows models to ingest text all at once and in parallel, significantly improving processing efficiency.