source: “How much compute does the world really need? Scale cannot solve AI’s fundamental problem with accuracy”
COMPUTING
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Nvidia’s biggest moat is not CUDA or GPUs
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It's actually way dumber than that. CUDA is not even the biggest Nvidia moat. And neither are the GPUs. Both of those could be completely commoditized tomorrow and it would hardly have any impact on Nvida's AI infra dominance.
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GLM 5.2 drives enterprises off cloud, checkmate for open-source AI
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Thanks to GLM 5.2, I know for a fact that enterprises are moving off the cloud, acquiring compute, and working on having post-trained models for their own use cases. It's checkmate for Opensource AI, they just don't know it yet.
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Latency and copyright issues leave car AI market open
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Latency is atrocious. ChatGPT nails the latency, but also refuses to read texts from the internet to me, citing copyright concerns, even when agreeing that the text is in the public domain. The market for car companion AI is still wide open.
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GLM 5.2 MoE, NVFP4 467GB, DGX Station memory, offloading works
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GLM 5.2 is an MoE, NVFP4 is 467 GB, and the DGX Station comes with 496GB LPDDR5X + 252GB HBM3e GPU memory With the right offloading formula, it should work
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Working on getting GLM 5.2 NVFP4 by Luke Alonso running
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Currently working on getting GLM 5.2 NVFP4 by Luke Alonso up and running 🙂 Will report back
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NVIDIA-accelerated AI aids PYLER in brand safety for advertisers
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Every day, millions of videos compete for advertising dollars. Ensuring brands appear alongside the right content requires AI that can understand context at scale.
— NVIDIA (@nvidia) 25 juin 2026
PYLER is helping advertisers improve brand safety and campaign performance with NVIDIA-accelerated AI that analyzes… pic.twitter.com/9xSDjj9e9gEvery day, millions of videos compete for advertising dollars. Ensuring brands appear alongside the right content requires AI that can understand context at scale. PYLER is helping advertisers improve brand safety and campaign performance with NVIDIA-accelerated AI that analyzes
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ViT³: Test-Time Training Replaces Attention with Online Learning
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Why settle for attention when you can learn at test time? Tsinghua University & Alibaba Group present ViT³: a pure Test-Time Training (TTT) architecture that replaces attention with an online learning model built from key-value pairs. This inner model trains on the fly,
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User finds local LLMs painfully slow on single RTX 5090
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And yes I've tried local LLMs but with just 1x RTX 5090 it's painfully slow and useless
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SambaNova’s fastest inference cloud and Ricoh’s custom Japanese AI models
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Customers Scaling AI Faster General Compute @fastinference launched the world's fastest inference cloud for AI agents, powered by SambaNova. Meanwhile, @ricoh is using SambaCloud for custom Japanese AI models and agentic business workflows, moving from tens of tokens per