Why does this matter for AI inference specifically? Training = throughput problem. Inference = latency problem. When a user talks to an AI assistant, tokens have to return fast. Latency, memory access, bandwidth, and interconnect all matter, not just raw compute. In large AI
HARDWARE
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Huawei’s Tau Scaling Law redefines AI inference bottleneck
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Most AI teams are optimizing the model.
— Ronald van Loon (@Ronald_vanLoon) 8 juin 2026
But the real bottleneck in inference is underneath it.
Huawei's Tau Scaling Law (Her's Law) was just introduced at IEEE ISCAS in Shanghai.
It reframes how we think about AI performance entirely.
Here's the breakdown…#HuaweiPartner… pic.twitter.com/MvwfNu7ZisMost AI teams are optimizing the model. But the real bottleneck in inference is underneath it. Huawei's Tau Scaling Law (Her's Law) was just introduced at IEEE ISCAS in Shanghai. It reframes how we think about AI performance entirely. Here's the breakdown… #HuaweiPartner
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Confirming the growing popularity of edge models
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Can confirm, edge models keep getting more and more popular
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Huawei electric powertrain and autonomous driving platform used by Chinese carmakers
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Huawei has developed an electric power train and autonomous driving platform that is being used by incumbent Chinese carmakers through new brands like AITO. The blue light tells you that the car is driving itself…
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Love stumbling upon tears advising to buy GPU for local models
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I just love randomly stumbling upon tears that say “Buy a GPU and run your own models locally! “
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AI crosses the threshold of recursive self-improvement
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AI has just crossed a threshold: recursive self-improvement. Models write the code that trains them, discover the algorithms that will make them better, design the chips that run them… The machine improves the machine, which will improve the machine.
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Printer can now print images using Epson FX-86e driver and raw data decoding
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🖨️🖼️ Big progress: it can now print images too
— @levelsio (@levelsio) 7 juin 2026
I had to install a different printer driver, Claude Code recommended Epson FX-86e which supported images
Then I printed an image, it helped me dump a .bin from the printer raw data output, then it decoded the .bin and figured out… https://t.co/S6n7Z5koOa pic.twitter.com/StqNvf8bzuBig progress: it can now print images too I had to install a different printer driver, Claude Code recommended Epson FX-86e which supported images Then I printed an image, it helped me dump a .bin from the printer raw data output, then it decoded the .bin and figured out
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Fine-tuning Gemma 4 12B to master chess on 8GB VRAM
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Google released Gemma 4 12B, a multimodal model that runs text, images, and audio on 8GB VRAM!
— Akshay 🚀 (@akshay_pachaar) 7 juin 2026
We'll fine-tune it to master chess and predict the exact next move.
Tech stack:
– @UnslothAI for efficient fine-tuning.
– @huggingface transformers to run it locally.
Let's go! 🚀 pic.twitter.com/qPXjlYrShsGoogle released Gemma 4 12B, a multimodal model that runs text, images, and audio on 8GB VRAM! We'll fine-tune it to master chess and predict the exact next move. Tech stack:
– @UnslothAI for efficient fine-tuning.
– @huggingface transformers to run it locally. Let's go! -
Lack of useful tasks for 330,000 H100s questions scaling hypothesis
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fair point that the contracts aren’t long term, but it still doesn’t speak well for their confidence in their original scaling hypothesis if they can’t find something useful to do with 330,000 H100s.
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xAI/SpaceX becomes neo-hyperscaler for frontier AI compute
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xAI/SpaceX is increasingly becoming an AI infrastructure player, potentially one of the most important „neo-hyperscalers” for frontier AI compute. Grok is good, but its user base remains comparatively small. In that sense, repurposing Colossus to rent out compute capacity is a