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The real metric that matters? GPU utilization. With Aethir, customers like DCENT - Driving DePIN growth with Sustainable Edge see utilization rates reaching up to 100%, making ROI clearer & GPU scaling more efficient. DCENT’s Founder & CEO, Hidde, explains how they quantify performance and cost 👇📹

Aethir Ecosystem

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21,281 次观看 • 7 个月前 •via X (Twitter)

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"Aethir is the fabric between DCENT and its clients" DCENT struggled with GPU scaling and utilization… until they moved to Aethir. DCENT - Driving DePIN growth with Sustainable Edge’s founder & CEO, Hidde, explains the shift perfectly, a real look at the impact Aethir delivers. Worth watching 👇🏻

"Aethir is the fabric between DCENT and its clients" DCENT struggled with GPU scaling and utilization… until they moved to Aethir. DCENT - Driving DePIN growth with Sustainable Edge’s founder & CEO, Hidde, explains the shift perfectly, a real look at the impact Aethir delivers. Worth watching 👇🏻

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53,118 次观看 • 5 天前

Participants in the GPU rental market had grown accustomed to a familiar regime: relentless cost declines, falling $/token, and the assumption that each new chip generation would compress pricing further. Many onlookers extrapolated this into outright bearishness on older hardware, calling for a wave of impairments across AI clouds as Hopper-era GPUs were rendered economically obsolete. As Dylan noted, the logic seemed straightforward - newer chips dramatically outperform Hopper, so prices should fall. The rise of agentic AI have shifted the paradigm. Pricing is now repricing violently to real-time economic value under constraint. As highlighted in the transcript, even though models like GPT-5.4 are cheaper and more efficient - producing more tokens per GPU at higher quality - this does not reduce demand. Instead, it expands it, in-line with Jevons' Paradox. SemiAnalysis data shows the same dynamic: token usage is accelerating rapidly because the ROI is obvious, making compute demand increasingly price insensitive. In this new regime, supply constraints and token value dominate pricing we think this paradigm is only here to stay if not intensify.

Participants in the GPU rental market had grown accustomed to a familiar regime: relentless cost declines, falling $/token, and the assumption that each new chip generation would compress pricing further. Many onlookers extrapolated this into outright bearishness on older hardware, calling for a wave of impairments across AI clouds as Hopper-era GPUs were rendered economically obsolete. As Dylan noted, the logic seemed straightforward - newer chips dramatically outperform Hopper, so prices should fall. The rise of agentic AI have shifted the paradigm. Pricing is now repricing violently to real-time economic value under constraint. As highlighted in the transcript, even though models like GPT-5.4 are cheaper and more efficient - producing more tokens per GPU at higher quality - this does not reduce demand. Instead, it expands it, in-line with Jevons' Paradox. SemiAnalysis data shows the same dynamic: token usage is accelerating rapidly because the ROI is obvious, making compute demand increasingly price insensitive. In this new regime, supply constraints and token value dominate pricing we think this paradigm is only here to stay if not intensify.

SemiAnalysis

55,206 次观看 • 4 个月前

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Aish

27,268 次观看 • 1 年前

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andrew engler

1,722,128 次观看 • 27 天前

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

Fireside Alpha

175,586 次观看 • 21 天前