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AI Infrastructure

113 summarised stories about AI Infrastructure, each linking back to the original source. Browse all topics →

Friday, 10 July 2026

“Eastern Data, Western Compute” is Fake

ChinaTalk 6 days ago

China's "Eastern Data, Western Compute" policy, intended to shift data center infrastructure westward, has largely failed to materialize as promoted, with 94% of China's population and most computing capacity remaining in eastern and exurban regions rather than remote western provinces. Analysis of actual chip distribution shows the top data center locations are concentrated in Hebei, Guangdong, Jiangsu, and Guizhou, with the real pattern being expansion into exurbs around major eastern cities rather than genuine westward movement, driven by practical constraints including labor shortages, latency issues, and semiconductor supply constraints. Poorer western provinces may face mounting debt from speculative data center projects built on unrealistic development assumptions, while the policy's original promise to help interior regions become meaningful AI economy participants remains unfulfilled.

Real-time dental image verification with Amazon SageMaker AI at Henry Schein One

AWS Machine Learning 6 days ago

Henry Schein One deployed Image Verify, an AI system built on Amazon SageMaker that evaluates dental X-ray quality in real time at the point of capture to reduce rejected insurance claims caused by poor image quality. The system reached over 10,000 active locations processing 1.5 million X-rays weekly with a median latency of 1.4 seconds and 0.01 percent error rate, achieved through GPU optimization and multi-region deployment. By catching low-quality images before patients leave, Image Verify reduces patient callbacks, improves claim acceptance, and eliminates costly retakes.

Disaggregated prefill and decode for LLM inference on SageMaker HyperPod

AWS Machine Learning 6 days ago

Amazon SageMaker HyperPod now supports Disaggregated Prefill and Decode (DPD), which separates LLM inference into compute-bound prefill and memory-bound decode phases running on separate GPU pools connected via Elastic Fabric Adapter. The implementation uses vLLM with LMCache to handle long-context, high-concurrency streaming workloads, with KV cache transfer taking single-digit milliseconds on ml.p5.48xlarge instances. Organizations can now independently tune time to first token and inter-token latency while preventing long prompts from blocking concurrent decode requests.