The economics of GPUs: How to train your AI model without going broke

Many companies are eager to integrate AI into their businesses but are hindered by the high costs associated with training sophisticated AI systems, particularly due to the expensive hardware required, like GPUs. Elon Musk has highlighted that engineering challenges often stall progress, especially in optimizing hardware for AI. While large tech companies can afford the hefty costs of training large language models (LLMs), smaller businesses with limited resources struggle. However, there are strategies available to help these smaller players. One hardware-focused strategy involves optimizing training hardware, with examples like custom AI chips and rental GPUs. However, this approach is more feasible for big companies with deep pockets. For smaller companies, software-based optimizations offer a more accessible and cost-effective alternative. One such method is mixed precision training, which optimizes memory usage and speeds up training by using lower precision operations. This technique can lead to significant runtime improvements and reduce GPU costs. Another approach, activation checkpointing, minimizes memory consumption by storing only essential values during training, though it extends training time slightly. Multi-GPU training is another strategy that speeds up the training process by distributing tasks across multiple GPUs. Tools like DeepSpeed, FSDP, and YaFSDP help implement this method, with each tool offering incremental efficiency gains. By employing these innovative software and hardware strategies, companies with limited resources can still train and develop AI models without incurring exorbitant costs.