Cloud to edge AI: If and when, to what degree, and how and why

What happened

This is an opinion/analysis article arguing that AI processing, specifically inference, remains mostly cloud-centric but is shifting toward edge devices, and that storage and memory requirements will keep rising in both cloud and edge scenarios. According to the article, data center build-outs are redirecting supplies of SSDs (and HDDs) and DRAM, while the required capacities in PCs, smartphones and the like for today's edge AI processing are increasing in lockstep: the Gemini AI models embedded in latest-generation Chrome browser installs take up at least 4 GBytes of storage space; Apple Intelligence included with iOS 27 is now required versus optional with the version 26 predecessor and uses up to 14 GBytes; the required-by-default AI model suite included with MacOS 27 is 12-30 GBytes in size. PrismML compressed Alibaba's Qwen3.8 27B open-source model by 9-10x while still matching 98% of Qwen's aggregate benchmark scores, but the resultant model was still 5.9 GBytes; Perplexity's Portable Computer for local on-computer model storage and processing uses 20 GBytes. The article also states that the training run for OpenAI's latest-generation "Astra" GPT-6 fully consumed more than 100,000 GPUs, and argues that hybrid approaches (local base-level inference with cloud tapped as needed) will become increasingly common, with the decode portion of inference being memory-intensive from both capacity and bandwidth standpoints, rationalizing advanced HBM approaches. All points are the author's analysis; the source reports no price changes.

Impact and signal

Affected components and suppliers

Source and time

Source: EDN · 10/29/2026 · View original source