Volantis ha raccolto 88 milioni di dollari in Serie A per risolvere il collo di bottiglia della memoria dell'IA con ottica

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1. [34] Whale Insider · @whaleinsider · 05:16

   JUST IN: Volantis raises $88M Series A to tackle AI’s memory bottleneck with optics, enabling chips with massive fast & cheap memory for ultra-fast inference on models over 10T parameters.

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2. [43] CBS News · @cbsnews · 03:46

   BREAKING: President Trump is likely to pick director of national intelligence Jay Clayton to be his AI czar, sources say.

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3. [46] Backpack 🎒 · @backpack · 03:34

   Introducing Backpack AI Search. Ask Backpack to find it. A stock. A strategy. A market condition. Anything you can put into words. Stocks. Crypto. Perps. Earn. One search bar across Backpack. Just ask.

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4. [50] Not Jerome Powell · @alifarhat79 · 02:31

   Volantis just raised $88M to solve AI’s memory problem with literal light. 10,000 tokens/sec. 10T+ models. Agents that finish in seconds, not hours. Apparently the next AI scaling law is: turn the RAM into a laser beam.

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5. [51] Shay Boloor · @stocksavvyshay · 02:28

   Volantis just raised $88M from investors including $GOOGL Jeff Dean to build optical AI chips that ease the memory bottleneck in inference. Interesting timing right after $MU’s record quarter with Volantis betting light can make agent workloads dramatically faster.

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6. [55] Wall St Engine · @wallstengine · 02:04

   $GOOGL JUST PUT ITS AI CHIPS INTO ORBIT FOR THE FIRST TIME Google and Planet Labs $PL have successfully launched Project Suncatcher M1 aboard SpaceX’s Transporter-18 mission. Planet says it has already made contact with the spacecraft and begun commissioning. This is the first-ever in-orbit test of Google’s Tensor Processing Units, or TPUs. The refrigerator-sized prototype carries four Trillium TPUs, Google’s custom AI accelerators. This is not yet an orbital data center. The goal is to see whether the same type of AI hardware Google uses on Earth can survive launch and reliably run machine-learning workloads in low Earth orbit. There are three major engineering problems Google is testing: RADIATION: cosmic radiation can corrupt calculations or damage electronics. Google previously tested Trillium TPUs with proton radiation and found they could withstand more total radiation than expected over a five-year mission. COOLING: there is no air in space, so fans cannot cool the chips. Heat has to be moved through heat pipes into radiators and then emitted away from the spacecraft. The current prototype is expected to run AI workloads in roughly 15-minute bursts before allowing the system to cool. NETWORKING: Google’s long-term plan is not one satellite, but clusters of satellites carrying dozens of TPUs each and connected through high-speed laser links. Google estimates future AI workloads could require tens of terabits per second between satellites. Its ground prototype has already demonstrated 800 Gbps in each direction. Why put AI compute in space at all? In the right low-Earth orbit, solar panels can generate up to 8x more power than on Earth because satellites can receive near-continuous sunlight, reducing one of the biggest constraints facing AI data centers: electricity. The economics: Google estimates that if launch costs eventually fall below roughly $200 per kilogram by the mid-2030s, the cost of operating compute in space could begin approaching the energy cost of comparable terrestrial data centers.

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