Will GenAI scale without enough power to accelerate multi-trillion parameter LLM training, causing economic fallout and impacting GDP growth? The rapid expansion of Generative AI (GenAI) and Large Language Models (LLMs), like GPT, Gemini and LLaMA, has drastically increased the need for computational power, especially as models scale from billions to trillions of parameters. Training these multi-trillion parameter models demands specialized hardware, notably NVIDIA’s H100 and upcoming GB200GPUs, which are designed to handle the immense Teraflop computation processing requirements of such massive model parameters count and datasets. These GPUs outperform traditional models in both speed and efficiency, but at the costof significantly higher power consumption.
Read MoreWhether it be through our PCs or the smart devices we carry around, computing controls almost everything we do in the modern world. Futurists view computational power and related capacities as key indicators of technological advancement. The computational explosion of data will have a direct impact on the fundamental pillars of society, such as healthcare, security, communications, transportation, and energy.
Read MoreIn 2021, Booz Allen Hamilton analysts surmised that China will surpass Europe and the US in quantum-related research and development and that Chinese hackers could soon target heavily encrypted datasets such as weapon designs or details of undercover intelligence officers with a view to unlocking them at a later date when quantum computing makes decryption possible.
Read MoreWhile AI-powered devices and technologies have become essential parts of our lives, machine intelligence still contains areas wherein drastic improvements can be made. To fill in these metaphorical gaps, non-AI technologies will come in handy.
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