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AI compute accelerated, emissions decelerated. ⏩🌍⚡ #AWS new data center components reduce emissions by up to 90% while offering 12% more compute power. Speed up #generativeAI innovation with the ultimate performance & sustainability combo. 👉

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Amazon Web Services

2,231,207 subscribers

22,386 просмотров • 1 год назад •via X (Twitter)

Наука и технологии#AWS#generativeAI
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Фото профиля Purple Pepe
Purple Pepe1 год назад

$Purpe AWS: where compute meets tranquility, and Ribbit saves the planet one efficiency upgrade at a time

Фото профиля Joe Stevens
Joe Stevens1 год назад

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We're teaming up with Cerebras to build the fastest possible inference. Coming soon to Amazon Bedrock, we’re delivering inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode. Learn more about the partnership.
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🚨 SpaceX is partnering with Anthropic to boost Claude’s compute power Anthropic will reportedly use SpaceX’s 300MW Colossus-1 data center, helping Claude raise API limits, reduce throttling, and improve performance. The interesting part? Elon Musk is helping a major OpenAI competitor while xAI shifts training to Colossus-2. This looks less like a compute shortage, and more like SpaceX turning AI infrastructure into a business. ------------ CyberBulls E190 The Bulls: Jeff Lutz Jeff Lutz 🔋 Cern Basher Cern Basher Phil Beisel phil beisel $TSLA
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🚨 SpaceX is partnering with Anthropic to boost Claude’s compute power Anthropic will reportedly use SpaceX’s 300MW Colossus-1 data center, helping Claude raise API limits, reduce throttling, and improve performance. The interesting part? Elon Musk is helping a major OpenAI competitor while xAI shifts training to Colossus-2. This looks less like a compute shortage, and more like SpaceX turning AI infrastructure into a business. ------------ CyberBulls E190 The Bulls: Jeff Lutz Jeff Lutz 🔋 Cern Basher Cern Basher Phil Beisel phil beisel $TSLA

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Scalable, grid-connected infrastructure is essential to meet the projected surge in AI data center demand, expected to reach 156GW globally by 2030. $IREN's Horizon 1 project unlocks up to 50MW of AI-ready capacity, built on the design foundation of our existing 650MW of powered shells at Childress to enable rapid deployment and scalability. As the need for high-density compute accelerates, Horizon 1 marks the first step in $IREN's long-term roadmap to power the next wave of AI innovation.

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AWS has activated Project Rainier, one of the world’s largest AI compute clusters with nearly 500,000 Trainium2 chips brought online in under a year. Anthropic is already training and serving Claude on the system and is slated to scale past 1 million Trainium2 chips by the end of 2025. The UltraServer/UltraCluster design (NeuronLinks + EFA) speeds massive distributed training, while AWS’s vertically integrated stack and data-center efficiency targets aim to cut cost and environmental impact.

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AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

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AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

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🚀 Elon Musk has confirmed STARMIND as the name of SpaceX's future AI satellite network. FutureAzA says this is just one piece of a much bigger strategy. SpaceX is rapidly building an AI compute business, with reported customers including Google, Anthropic, and Reflection AI. The Reflection agreement alone is reportedly worth up to $6.3 billion through 2029. At the same time, Tesla continues expanding its energy business with a 25 GWh storage agreement through NatPower and a new partnership with Sunrun and Renew Home to help power AI infrastructure. AI, energy, compute, and space are starting to come together into one ecosystem. The more you zoom out, the more connected Tesla and SpaceX begin to look. $TSLA $SPCX

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Starcloud (starcloud_inc1) recently made history by launching a satellite with an NVIDIA H100 into orbit — the first time a GPU that powerful has ever operated in space. It's the first step toward building AI data centers in orbit, powered by continuous sunlight and cooled by radiating heat into deep space. Their approach could one day rival the world's biggest data centers while using less energy, zero fresh water, and far lower emissions. YC's Aaron Epstein visited Starcloud's HQ, where co-founders , Ezra Feilden, and Adrian Andrei Oltean explained how they built a working prototype in just 15 months — and why big tech is racing to space for AI compute.
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Starcloud (starcloud_inc1) recently made history by launching a satellite with an NVIDIA H100 into orbit — the first time a GPU that powerful has ever operated in space. It's the first step toward building AI data centers in orbit, powered by continuous sunlight and cooled by radiating heat into deep space. Their approach could one day rival the world's biggest data centers while using less energy, zero fresh water, and far lower emissions. YC's Aaron Epstein visited Starcloud's HQ, where co-founders , Ezra Feilden, and Adrian Andrei Oltean explained how they built a working prototype in just 15 months — and why big tech is racing to space for AI compute.

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Everyone’s racing to build AI. But most teams can’t even get started because infrastructure costs too much. io.net is solving that problem by connecting over 300K GPUs, 80K CPUs, and thousands of data centers worldwide into a single network anyone can access. By integrating with Irys, io.net would be able to offer developers a place to store training datasets, verifiable inference outputs, and entire AI models. With Irys, data is instantly accessible, fully transparent, and far more affordable than traditional or web3 storage. Spin up compute in seconds. Store data permanently. Build transparent, high-performance AI systems people actually trust. This is how we scale AI. Together.
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Everyone’s racing to build AI. But most teams can’t even get started because infrastructure costs too much. io.net is solving that problem by connecting over 300K GPUs, 80K CPUs, and thousands of data centers worldwide into a single network anyone can access. By integrating with Irys, io.net would be able to offer developers a place to store training datasets, verifiable inference outputs, and entire AI models. With Irys, data is instantly accessible, fully transparent, and far more affordable than traditional or web3 storage. Spin up compute in seconds. Store data permanently. Build transparent, high-performance AI systems people actually trust. This is how we scale AI. Together.

Irys (✧ᴗ✧)

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$SPCX Starmind AI satellites could route data through Starlink’s laser mesh avoiding complex onboard antennas and most direct spectrum requirements. That lets Starmind piggyback on more than 10,000 Starlink satellites with V3 models potentially carrying 2–3 Tbps each. This creates a distribution moat no standalone orbital-compute player can match but it also shifts the bottleneck to the ground by funneling enormous traffic through SpaceX’s gateways. Which is why SpaceX is expanding into E-, V- and W-band backhaul with D-band proposed for AI while new FCC rules replacing 1990s power limits could unlock up to 7x more capacity from the same satellite network.
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$SPCX Starmind AI satellites could route data through Starlink’s laser mesh avoiding complex onboard antennas and most direct spectrum requirements. That lets Starmind piggyback on more than 10,000 Starlink satellites with V3 models potentially carrying 2–3 Tbps each. This creates a distribution moat no standalone orbital-compute player can match but it also shifts the bottleneck to the ground by funneling enormous traffic through SpaceX’s gateways. Which is why SpaceX is expanding into E-, V- and W-band backhaul with D-band proposed for AI while new FCC rules replacing 1990s power limits could unlock up to 7x more capacity from the same satellite network.

Shay Boloor

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Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.
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Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

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31,285 просмотров • 3 месяцев назад