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Source channel @githubtrending · Post #15263 · Nov 2

#python#deep_learning#inference#llm#nlp#pytorch#transformer Nano-vLLM is a small, fast, and easy-to-understand tool for running large language models offline. It matches the speed of bigger systems like vLLM but uses only about 1,200 lines of clean Python code, making it simple to read and modify. It includes smart features like prefix caching and tensor parallelism to boost performance. You can install it easily and run models like Qwen3-0.6B on your own GPU. This tool is great if you want fast, efficient AI inference without complex setups, ideal for learning, research, or small deployments on limited hardware. https://github.com/GeeeekExplorer/nano-vllm

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Venture Village Wall 🦄

@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