“If there is a deflation of the AI bubble, the optimists say that the new infrastructure will remain even if the companies do not — just as railways survived the 19th-century railway bust. However, this fails to reckon with the reality of depreciation (few pieces of silicon hold
HARDWARE
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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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Almost added home GPU rig but local LLM lacks cloud quality
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So I almost added
– Home GPU rig And it might be nice for self-sufficiency but again I still don't think local LLM stuff even comes close to cloud either in quality or performance (speed) or cost Making your home self-sufficient is nice though, I have:
– 2x Tesla Powerwall (27 -
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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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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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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Gemma 4 prioritizes local on-device intelligence across hardware classes
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Gemma 4 is best in class at each hardware class, not designed to compete on server side frontier intelligence like GLM, it’s designed to enable local on device intelligence without needing advanced hardware
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SambaNova demonstrates first disaggregated inference cloud for AI agents
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Disaggregated Inference Is Live! At COMPUTEX, we demonstrated the world's first disaggregated inference cloud for AI agents.
GPUs for prefill. RDUs for decode. CPUs for orchestration. The result: faster agent workflows and better economics than homogeneous infrastructure. -
Deep Learning with C++ using CUDA for high-performance AI book
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Deep Learning with C++ — Design and deploy neural networks using CUDA for high-performance AI in C++ Get the book at http://
amzn.to/4nzdKB4 from @PacktPublishing @PacktDataML -
OpenAI and Broadcom’s AI chip development raises funding questions
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really interesting look from @dinabass at the long-in-the-works effort by @OpenAI and @Broadcom to develop an AI chip that can make models run faster and cheaper. still some key q's, like where is all the $$$ coming from to pay for this?