The six AI model families and what they're good for

AI model selection is presented as the first major infrastructure-sizing decision because different neural-network families consume different data, perform different tasks, and require distinct hardware profiles. Transformer-based language models power text generation, question answering, summarization, code completion, and document tools but are particularly costly and memory-intensive, while computer vision models support applications such as medical imaging, manufacturing inspection, moderation, and autonomous vehicles. Generative models, including GANs and diffusion systems, create images, video, speech, and other media for uses such as marketing and product design, whereas speech-recognition models enable transcription, captioning, analytics, and voice interfaces. Reinforcement learning is useful for robotics, control systems, and logistics optimization, and multimodal, time-series, tabular, and edge-focused models address additional specialized needs. Because these architectures rely on highly parallel matrix and tensor operations with substantial memory demands, GPUs are generally central to AI infrastructure, although workload requirements vary substantially. Organizations are advised to classify each use case before purchasing hardware, avoiding the common mistake of sizing all projects around the most demanding model; for example, chatbots, transcription, and image generation should be treated as separate LLM, speech, and diffusion workloads.

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