1875 summarised stories about AI Infrastructure, each linking back to the original source. Browse all topics →
OpenAI 10 years ago 13
A group's deep learning infrastructure acts as a multiplier on research progress, determining how effectively teams can conduct experiments and develop models. Open-source tools and frameworks are now mature enough that researchers without specialized resources can build competitive infrastructure setups. This democratization of tools means infrastructure quality no longer primarily depends on institutional funding or proprietary systems.
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TLDRocket
21 October 2026
The day’s biggest throughline wasn’t new model announcements—it was the messy, expensive reality of getting AI to run at scale, profitably, and reliably. Google Cloud shipped llm-d 1.0, a Kubernetes-native distributed inference layer that routes requests to pooled vLLM or SGLang servers, while Google also unveiled TorchTPU, a PyTorch-focused path for developers trying to move off Nvidia GPUs (notably, the pitch leans on PyTorch’s dominance, cited as 85% of AI apps). Off the hardware side, Dutch startup Photon Bridge raised €10 million to build laser tech for optical links in large AI clusters, targeting first samples in 2027 and volume manufacturing in 2028, and framing the bottleneck as a manufacturing-scale problem: millions of lasers per year, delivered via multiplexing designed for higher yield.
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