Traditional silicon chips lose significant energy as heat during electricity conversion, requiring additional cooling systems that consume even more energy, creating a 'double penalty' in data centers. Wide bandgap semiconductors like gallium nitride and silicon carbide solve this problem by switching electricity faster, handling higher voltages, and generating less heat, which reduces energy losses by 20-40% and eliminates the need for extensive cooling systems, making them essential for energy-efficient AI data center infrastructure.
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This NEXT GEN AI Chips company is the picks and shovels play for #aiinfrastructure #datacenterAñadido:
Did you know that up to 40% of an AI data centers entire energy budget goes purely to cooling, not even running the actual GPUs? And ticker symbol NVTS is the picks and shovels play that's directly solving that issue. Now, here's how traditional silicon chips work. So, energy goes in, a chunk of it gets lost in the conversion process, and that lost energy escapes as heat. And now you need cooling to manage that heat, in which that cooling burns even more energy. And so, you're paying a double penalty every single time. Now, here's where Navitas comes in. They use gallium nitride and silicon carbide. They are materials that switch electricity faster, handle higher voltages, and generate less heat. So, the chain flips. So, energy goes in, very little is lost, minimal heat, and less cooling is needed. And so, more energy is saved, and it compounds in your favor. No double penalty. Now, remember, one large AI data center consumes as much electricity as one small city. If Navitas cuts energy losses by 20 to 40%, you'll be eliminating entire cooling systems and running more GPUs in the same place. And that's why hyperscalers are paying very close attention. Now, this is just surface level information and analysis.
So, if you want the full breakdown, go to the C Apple Swing the Watch report using the link in my bio.
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