#aiscaling — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aiscaling, aggregated by home.social.
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Whenever someone declares #LLMs ded, check which wall they're pointing at, there are three and they keep getting smashed together.
Attached plot: the one people wave around as a ceiling. Log-log axes though, that dashed diagonal is a power law holding over ten orders of magnitude. Chinchilla et.all.
All it prices is compute against loss.The micro-outage forensics; failed requests, ergo no headroom, ergo scaling is dead, are fun but underdetermined.
Deploys, autoscaler lag and load-shedding all look identical to 'ran out of GPUs' from userland.
Frozen weights don't get tired: capacity pressure fails loudly, not stupid. The honest symptom is the 502. The dishonest one is quiet , quantize harder, trim context, route to the smaller model, same name on the box. Providers do that.sometimeThe money, though, sure fair cop. A quarter-trillion vendor backstop should make everyone's eye twitch. HOOOWEVER... (And its not something I wanna argue, still developing this argument)...
A quarterly return is a shit metric for a magic genie that will put all the professionals out of work, forever.
We all know capitalism sucks, and its funny that brologarchs are discovering it too.Just don't let a shaky business model stand in for a broken scaling law. Power laws have outlived plenty of companies.
TLDR; Yes but no.
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Whenever someone declares #LLMs ded, check which wall they're pointing at, there are three and they keep getting smashed together.
Attached plot: the one people wave around as a ceiling. Log-log axes though, that dashed diagonal is a power law holding over ten orders of magnitude. Chinchilla et.all.
All it prices is compute against loss.The micro-outage forensics; failed requests, ergo no headroom, ergo scaling is dead, are fun but underdetermined.
Deploys, autoscaler lag and load-shedding all look identical to 'ran out of GPUs' from userland.
Frozen weights don't get tired: capacity pressure fails loudly, not stupid. The honest symptom is the 502. The dishonest one is quiet , quantize harder, trim context, route to the smaller model, same name on the box. Providers do that.sometimeThe money, though, sure fair cop. A quarter-trillion vendor backstop should make everyone's eye twitch. HOOOWEVER... (And its not something I wanna argue, still developing this argument)...
A quarterly return is a shit metric for a magic genie that will put all the professionals out of work, forever.
We all know capitalism sucks, and its funny that brologarchs are discovering it too.Just don't let a shaky business model stand in for a broken scaling law. Power laws have outlived plenty of companies.
TLDR; Yes but no.
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Whenever someone declares #LLMs ded, check which wall they're pointing at, there are three and they keep getting smashed together.
Attached plot: the one people wave around as a ceiling. Log-log axes though, that dashed diagonal is a power law holding over ten orders of magnitude. Chinchilla et.all.
All it prices is compute against loss.The micro-outage forensics; failed requests, ergo no headroom, ergo scaling is dead, are fun but underdetermined.
Deploys, autoscaler lag and load-shedding all look identical to 'ran out of GPUs' from userland.
Frozen weights don't get tired: capacity pressure fails loudly, not stupid. The honest symptom is the 502. The dishonest one is quiet , quantize harder, trim context, route to the smaller model, same name on the box. Providers do that.sometimeThe money, though, sure fair cop. A quarter-trillion vendor backstop should make everyone's eye twitch. HOOOWEVER... (And its not something I wanna argue, still developing this argument)...
A quarterly return is a shit metric for a magic genie that will put all the professionals out of work, forever.
We all know capitalism sucks, and its funny that brologarchs are discovering it too.Just don't let a shaky business model stand in for a broken scaling law. Power laws have outlived plenty of companies.
TLDR; Yes but no.
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CTO confidence in scaling AI is down — even as adoption climbs. The real hurdle now? Governance, trust, and integrating AI into the enterprise. https://jpmellojr.blogspot.com/2026/06/ctos-face-growing-challenges-scaling-ai.html #AI #CTO #Akkodis #AIscaling
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CTO confidence in scaling AI is down — even as adoption climbs. The real hurdle now? Governance, trust, and integrating AI into the enterprise. https://jpmellojr.blogspot.com/2026/06/ctos-face-growing-challenges-scaling-ai.html #AI #CTO #Akkodis #AIscaling
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Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
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Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
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Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
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Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
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Alibaba’s Aegaeon System Slashes AI Inference Costs by 82% with Smart GPU Scheduling
#AI #Alibaba #CloudComputing #AIInference #AIEfficiency #AIScaling #ChinaAI
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Microsoft's venture arm just doubled funding for datacenter cooling software—not because efficiency is trendy, but because AI growth now outpaces grid capacity. When new power takes years, software that frees 40% of cooling energy becomes compute capacity. #DatacenterInfrastructure #AIScaling
https://www.implicator.ai/microsoft-backs-german-ai-firm-etalytics-as-datacenter-power-costs-bite/
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Microsoft's venture arm just doubled funding for datacenter cooling software—not because efficiency is trendy, but because AI growth now outpaces grid capacity. When new power takes years, software that frees 40% of cooling energy becomes compute capacity. #DatacenterInfrastructure #AIScaling
https://www.implicator.ai/microsoft-backs-german-ai-firm-etalytics-as-datacenter-power-costs-bite/
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"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
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"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
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"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
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"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
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"What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect."
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🎷 What if AI worked like a jazz band — only the essential players performing at the perfect moment?
🚀 Grok 3’s Mixture of Experts activates just 2 of 32 experts at a time, achieving 4× power at 60% cost.
👉 Discover how smart architecture is reshaping AI’s future!
#MoE #Grok3 #SparseAI #AIScaling
https://medium.com/@rogt.x1997/inside-the-silent-surge-the-untold-power-of-mixture-of-experts-and-grok-3-2bf00bb89247 -
China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
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China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
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China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
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China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
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China's Tencent Cuts GPU Demand by Turning to DeepSeek's Efficient AI Models
#AI #Tencent #DeepSeek #AIModels #GPUs #AIInfrastructure #ChinaAI #AIEfficiency #AIScaling #AIReasoning #ModelOptimization
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This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
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This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
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This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
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This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
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This New AI Scaling Method Challenges Scaling Laws — But Can It Deliver?
#AI #AIResearch #AIScaling #MachineLearning #Inference #AIModels #GenAI #TechInnovation #AIPerformance #AIEfficiency #DeepLearning
