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Renewable energy storage? Yes, it's possible! 👏🏻 We have two options: ✅Batteries ✅Pumped hydroelectric plants Learn more about these storage solutions in this video 📹

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🤝CM Devendra Fadnavis presided over the MoU signing and exchange between the Water Resources Department, Government of Maharashtra and various companies for Pumped Storage Hydroelectric Projects. Details of MoU: 🔸JSW Neo Energy Ltd Western Ghats Pumped Storage Hydropower Project, Pune and Raigad ✅ Installed Capacity - 5200 MW ✅ Investment - ₹19,950 crore ✅ Employment Generation - 7000 🔸MAHAGENCO RENEWABLE ENERGY LIMITED Koyna Stage - 6 Pumped Storage Hydropower Project, Taluka Patan, Dist. Satara ✅ Installed Capacity - 400 MW ✅ Investment - ₹2800 crore ✅ Employment Generation - 2500 🔸NEW ASIAN INFRASTRUCTURE DEVELOPMENT PVT LIMITED Sarovar Pumped Storage Hydropower Project, Akole, Dist. Ahilyanagar. ✅ Installed Capacity - 200 MW ✅ Investment - ₹1050 crore ✅ Employment Generation - 2000 Overall Installed Capacity 5800 MV, Investment ₹23,800 crore and the employment will be 11,500. 🤝मुख्यमंत्री देवेंद्र फडणवीस यांच्या प्रमुख उपस्थितीत, जलसंपदा विभाग, महाराष्ट्र शासन आणि विविध कंपन्यांमध्ये उदंचन (पंपस्टोरेज) जलविद्युत प्रकल्पासाठी सामंजस्य करार. सामंजस्य करारांची माहिती : 🔸जेएसडब्ल्यु निओ एनर्जी लिमिटेड पश्चिमघाट उदंचन जलविद्युत प्रकल्प, पुणे व रायगड. ✅स्थापित क्षमता - 5200 मेगावॅट ✅गुंतवणूक - ₹19,950 कोटी ✅रोजगार निर्मिती - 7000 🔸 महाजेनको रिन्युएबल एनर्जी लिमिटेड कोयना टप्पा-6 उदंचन जलविद्युत प्रकल्प, ता. पाटण, जि. सातारा ✅स्थापित क्षमता - 400 मेगावॅट ✅गुंतवणूक - ₹2800 कोटी ✅रोजगार निर्मिती - 2500 🔸न्यु एशियन इंफ्रास्ट्रक्चर डेव्हलपमेंट प्रायव्हेट लिमिटेड सरोवर उदंचन जलविद्युत प्रकल्प, अकोले, जि. अहिल्यानगर ✅स्थापित क्षमता - 200 मेगावॅट ✅गुंतवणूक - ₹1050 कोटी ✅रोजगार निर्मिती - 2000 एकूण स्थापित क्षमता 5800 मेगावॅट, गुंतवणूक ₹23,800 कोटी आणि एकूण रोजगार निर्मिती 11,500. Devendra Fadnavis #Maharashtra #DevendraFadnavis #Nagpur #Hydropower

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19,019 Aufrufe • vor 8 Monaten

Does the world have enough lithium to power all the electric vehicles and stationary batteries needed to transition the world to 100% clean, renewable energy and storage for everything? The answer is yes. In 2025, the USGS increased its estimate of world lithium resources over land by 30%, to 150 million tons, with the U.S. having the largest resource, 30 million tons, followed by Argentina, Bolivia, Chile, Australia, and China. How much lithium is this? The world has 1.1 billion passenger cars and 375 million trucks and buses. Replacing these requires about 47 million tons of lithium -- 9 million tons for the cars and 38 million tons for the trucks and buses. That’s only 31 percent of the 2025 known lithium resources, and keep in mind, the known resources grow each year as people look for more lithium. What is more, lithium stays in a vehicle during a battery’s 15 to 25-year life. At the end of the battery’s life, the battery is recycled or re-used for stationary electricity storage, so the mining is one-time. For stationary electricity storage itself, less than one-tenth the lithium, 2 to 4 million tons, is needed worldwide than is needed for vehicles. As such, current lithium resources are over three times those needed for vehicles plus storage. Also, many other battery types now exist that don’t use lithium. In sum, there is no shortage of lithium to transition the world to 100% clean, renewable energy and storage for everything. More info Video:

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Dylan Patel on the importance of memory and storage Two key quotes: "An $NVDA GPU is faster than an $AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads." “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly" Full Quote: “We have over $80 million of compute: GPUs from $NVDA and $AMD, TPUs from Google, and Trainium from Amazon. We constantly run this benchmark using the newest inference engines, drivers, PyTorch versions, and other software. It runs every day through automated CI across the latest Chinese models from GLM, Zhipu, Moonshot, Kimi, Alibaba, and others. Initially, when we were benchmarking the differences between these chips, inference engines, and parallelism schemes, we used fixed context lengths. But with Agent X, we have now analyzed more than $5 million worth of Claude Code traces. This is real production traffic that users have donated to us, combined with internally generated data, so we now understand what an actual agent workload looks like. When we implement those workloads and run the benchmarks, it turns out that the chip you are using is very important, but how you handle memory offload can be even more important. An Nvidia GPU is faster than an AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads. Similarly, you can use a less powerful GPU with a much better storage solution and outperform the best GPU when it lacks those solutions. Simply buying the newest GPU does not necessarily give you the best inference economics. You need to layer in other innovations, including storage and memory.” Interviewer: “Who is the top player on your chart? Can you tell us?” Dylan Patel: “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly.”

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