3/ First operational alpha systems before the end of the year. Full production in 2027. If Tensordyne actually delivers more than 1000 T/s on models with 1 or 2 trillion parameters at less than 10 USD/1MTs – they could really make a significant breakthrough on the
COMPUTING
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SRAM+HBM Hybrid with sub-1µs latency for parallel MoE decoding
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2/ Presented as the first true hybrid. Lots of on-chip SRAM (like Groq) combined with HBM (Nvidia). They claim their chip-to-chip fabric has super low latency (
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Chinese peak hours (14-18) cause 3x model cost, CEST 08-12
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Between 14:00-18:00 Chinese time it's peak hours and the model costs 3x, that's 08:00 to 12:00 CEST. People need to work it out in their own timezones…
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Manufacturers shift AI decisions to edge in factories
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This is why many manufacturers are rethinking where AI decisions happen, not just how good those models are. The shift is moving decision-making to the edge of the network, inside the factory itself. That means:
→ No dependency on distant cloud infrastructure
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Cloud latency problem in AI defect detection
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Here’s how the hidden latency problem works. A production line runs 24/7. Cameras inspect products in low-latency production environments. AI detects defects. But if that decision happens in the cloud, here’s what occurs:
→ Data leaves the factory → Gets sent to a remote -
NVIDIA and Corning announce long-term partnership for US AI manufacturing
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GLW NVIDIA and GLW Announce Long Term Partnership To Strengthen U.S. Manufacturing for AI Infrastructure! NVIDIA Corporation operates as a data center scale AI infrastructure company in the U.S.A. NVIDIA is making a major strategic investment in Corning to secure
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NVIDIA Achieves Leading Agentic Coding Performance on AI
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NVIDIA Achieves Leading Agentic Coding Performance on Agentic AI! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
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Inverse scaling: weak model gains from harness patching runtime gaps
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Exactly the inverse-scaling point. The weak model gained most because the harness patched gaps it couldn't fix on its own. Cheaper to evolve the runtime than to scale the model, and you keep the smaller footprint at inference.
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AI’s next bottleneck is time lost on data, chips, and memory
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AI’s next bottleneck is not just compute. It is time. Time lost moving data.
Time lost coordinating chips.
Time lost waiting on memory, interconnects, and software layers to catch up. That shift changes how we should think about AI infrastructure. A thread… #HuaweiPartner -
NVIDIA’s LocateAnything speeds object detection 10x by fixing coordinate bottleneck
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🚨 @NVIDIA just dropped LocateAnything, making object detection ~10x faster by fixing one core bottleneck:
— Charly Wargnier (@DataChaz) 17 juin 2026
How the model writes coordinates.
Standard AI models do visual grounding the slow way.
They predict coordinates piece by piece: Token 1, Token 2, Token 3, Token 4 etc.… pic.twitter.com/GAyr0taCbX@NVIDIA just dropped LocateAnything, making object detection ~10x faster by fixing one core bottleneck: How the model writes coordinates. Standard AI models do visual grounding the slow way. They predict coordinates piece by piece: Token 1, Token 2, Token 3, Token 4 etc.