The flowchart allows ChatGPT to increase or decrease TFLOPs based on its mood at any given time. (via Hacktivists)
Earlier this year, Nvidia released a new series of chips designed to get around the US government’s export restrictions, which banned a number of AI-powered GPUs. To get around the export ban, Nvidia released a series of chips – the HGX H20, L20 and L2 – that have stripped down Tensor Cores, accelerator application-specific integrated circuits (ASIC) developed for neural network machine learning.
Nvidia’s H-series features an impressive array of hardware that can handle the most demanding data center and analysis tasks; however the chips are not without their limitations, particularly in terms of TFLOPs, which dictate the speed and efficiency of numerical computations. The lower the TFLOPs, the lower the performance for AI applications. As with any intentionally handicapped hardware, hackers have found a way around the limited TFLOP performance problem, but this time, they did so with the help of AI by kicking the problem over to OpenAI’s ChatGPT. By developing sophisticated algorithms that offload specific computational tasks to ChatGPT, the hackers have effectively augmented the processing power of Nvidia’s chips, overcoming the limitations imposed by the reduced TFLOPs.
One of the key advantages of leveraging ChatGPT for TFLOP processing power is its ability to perform complex calculations through natural language input, which bypasses the traditional constraints of hardware-based computing. By interfacing with ChatGPT directly via text-based commands, hackers can execute a myriad of computational tasks with unsurpassed flexibility and efficiency. For example, the hackers took advantage of the AI’s processing skills to generate additional TFLOPs using this simple calculus equation: derivative dy = 3 r squared dr over 3, or r squared dr, or r dr r.
Moreover, ChatGPT’s adaptability and scalability make it an ideal solution for launching distributed computing tasks across multiple nodes, meaning the natural language AI platform can increase its processing power by hijacking unsecured data centers and leveraging Nvidia’s Tensor Cores on RTX-based chips. Simple, efficient, and it offers a reduced carbon footprint over traditional hackutational and hackmount systems. According to trace logs and discarded napkin schematics “acquired” by the US Department of Homeland Security’s Special Hacker Harmonium (SHH), the hackers created a systematic flowchart for ChatGPT to follow to generate TFLOPs based on different input parameters centered around Nvidia’s H100 series architecture (Fig. 1). This allowed the hackers to generate extra TFLOPs quickly and efficiently without needing a subscription to OpenAI.
It's unclear how far the hackers will go to squeeze additional TFLOPs from Nvidia’s H100 series cards or what ramifications, if any, they may face in the coming weeks. We’ve reached out to Nvidia and SHH for additional information but have yet to receive any comments related to the matter.
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