At the recent AI Infra Summit by Ignite GTM at @PlugandPlayTC they had me interview a few interesting founders. Here's Rahul Kar, founder of Hammerhead, http://
hammerheadco.ai, which is working to make power cheaper in AI datacenters. https://
youtu.be/cX-TE_PgZ3Q?si
=_uQPzVVwk9N8bA8Z
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HARDWARE
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Scobleizer interviews Rahul Kar on cheaper AI datacenter power
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First operational alpha systems, production 2027, low-cost Tensordyne
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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
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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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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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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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Humanoid robots move from demos to deployment with Physical AI
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Humanoid robots are moving from demos to deployment, and Physical AI is the reason.
— Bernard Marr (@BernardMarr) 17 juin 2026
Physical AI brings together perception, real-time learning, and embodied action, so robots can navigate human spaces, adapt to changing environments, and work safely alongside people.
In this… pic.twitter.com/48jA6OrxKUHumanoid robots are moving from demos to deployment, and Physical AI is the reason. Physical AI brings together perception, real-time learning, and embodied action, so robots can navigate human spaces, adapt to changing environments, and work safely alongside people. In this
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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 Blackwell Leads on Agentic AI Infrastructure
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NVIDIA Blackwell Leads on Agentic AI Infrastructure! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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NVIDIA thanks Satya Nadella for MLPerf record on Blackwell
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Thank you @satyanadella Excellent work with @Azure on one of the largest MLPerf Training submissions to date on NVIDIA Blackwell: 8,192 GPUs on NVIDIA GB200 NVL72 systems, Llama 3.1 405B training goal achieved in just 7.07 minutes. More to come
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SambaNova ready for RaiseSummit 2026: inference 2.0 with GPU and RDU
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We're ready for @RaiseSummit 2026 🙌
— SambaNova (@SambaNovaAI) 16 juin 2026
Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload.
GPUs for prefill. RDUs for decode. The right chip for the right job.
Join us at RAISE Summit in Paris to see what's next:… pic.twitter.com/FNThE5nXi8We are ready for @RaiseSummit 2026 Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload. GPU for prefilling. RDU for decoding. The right chip for the right task.