AI models have rapidly evolved from GPT-2 (1.5B parameters) in 2019 to models like GPT-4 (1+ trillion parameters) and DeepSeek-V3 (671B parameters, using Mixture-of-Experts). More parameters enhance context understanding and text/image generation but increase computational demands. Modern AI is now multimodal, handling text, images, audio, and video (e.g., GPT-4V, Gemini), and task-specific, fine-tuned for applications like drug discovery, financial modeling or coding. As AI models continue to scale and evolve, they require massive parallel computing, specialized hardware (GPUs, TPUs), and crucially, optimized networking to ensure efficient training and inference.
While computational power is an essential factor in AI development, optimized networking has emerged as a key enabler for maximizing AI efficiency and economic feasibility of large-scale AI initiatives.
The Hidden Costs of Suboptimal Networking
Many organizations diving into generative AI deployments focus primarily on computational power, often overlooking the crucial role of networking.
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