How I learned to stop worrying and love hyperscaler capex
The New Stack 2 months ago 33
The article reviews criticisms of hyperscaler spending on AI infrastructure and tests the claim that AWS, Google Cloud, and Azure are overspending on AI compute.
330 summarised stories about AI Cost & Economics, each linking back to the original source. Browse all topics →
The New Stack 2 months ago 33
The article reviews criticisms of hyperscaler spending on AI infrastructure and tests the claim that AWS, Google Cloud, and Azure are overspending on AI compute.
SiliconANGLE 2 months ago 44
Multi-tier storage architectures are being promoted to cut the cost of AI inference and improve performance by combining flash, object storage, and disk capacity tiers across training and inference pipelines. Samsung’s PM1723 drive is cited as delivering up to 28.4 gigabytes per second sequential read throughput and up to 6.6 million random read IOPS. Enterprises respond by redesigning storage tiering around KV-cache access and reducing data movement to better use GPU resources without relying only on more hardware.
Product Hunt 2 months ago 42
CodeBurn directs readers to see where their AI coding spend actually goes. The page is labeled a “Discussion” link, and it doesn’t provide any figures or dates in the text shown. No concrete change is described beyond pointing to that spending breakdown.
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The biggest throughline today wasn’t model architecture—it was the plumbing. DeepSeek open-sourced DeepInfer, the inference framework behind its production AI services, and backed it with numbers: one cluster serving 155 trillion tokens per day, plus off-peak pricing at $38 per GPU hour. DeepInfer now ships under an Apache 2.0 license on GitHub with support for DeepSeek-V4.1-Flash, while some performance-critical components remain tied to internal infrastructure and non-Nvidia determinism. In other words, the team is sharing the parts that get customers running, and keeping the bits that keep their edges sharp.
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