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Source channel @githubtrending · Post #14747 · May 25

#python#deep_learning#intel#machine_learning#neural_network#pytorch#quantization Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models. https://github.com/intel/intel-extension-for-pytorch

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@venturevillagewall · Post #3719 · 12/26/2024, 10:00 AM

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Nvidia's Earnings Shift Expected in 5 Years AI companies are struggling to earn enough to cover investments, according to David Kahn from Sequoia, who noted they should earn 6 times more. Currently, Nvidia leads AI earnings. SK Capital projects a shift in 3-5 years where solution developers like OpenAI will earn significantly more. This mirrors trends from the tech boom when software developers eventually outpaced hardware manufacturers. For detailed insights, read the full report here. #Nvidia#AI#Crypto#Sequoia#SKCapital#OpenAI#TechBoom#Earnings#Investments#MarketTrends#Software#Hardware#Developers#Graphics#Chips#GenerativeAI#InvestmentForecast#TechIndustry#BusinessModel#ValueChain#AIRevenue