{"id":477412,"date":"2026-02-15T23:42:13","date_gmt":"2026-02-15T23:42:13","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/477412\/"},"modified":"2026-02-15T23:42:13","modified_gmt":"2026-02-15T23:42:13","slug":"openclaw-fueled-ordering-frenzy-creates-apple-mac-shortage-delivery-for-high-unified-memory-units-now-ranges-from-6-days-to-6-weeks","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/477412\/","title":{"rendered":"OpenClaw-fueled ordering frenzy creates Apple Mac shortage \u2014 delivery for high Unified Memory units now ranges from 6 days to 6 weeks"},"content":{"rendered":"

Some Apple customers have recently been taken by surprise due to the order lead times on several Mac models with upgraded Unified Memory quotas, which could be largely driven by the immense popularity of OpenClaw, the locally-run open-source AI agent that\u2019s taking the internet by storm<\/a> and sending users scrambling for Macs to run the AI.<\/p>\n

While you can still get base model units of the MacBook Air<\/a>, iMac, M4 Mac mini<\/a>, and other basic models on the same day, upgrading memory can now increase delivery wait times by up to three weeks.<\/p>\n

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However, going for the highest possible memory capacity on high-end models greatly increases your waiting time, with the M3 Ultra Mac Studio<\/a> with 512GB of Unified Memory taking up to five to six weeks to be delivered.<\/p>\n

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Alex Finn, founder and CEO of Creator Buddy, linked this shortage in his X post to demand driven by \u201cthe world\u2019s first true AI agent” in reference to OpenClaw (previously Clawdbot\/Moltbot).<\/p>\n

Something big is happening. First Mac Minis. Now Mac Studios.Completely sold out.When I bought 2 Mac Studios a month ago my wait was 14 days. Now the wait is 54 days.The world has changed more in the last month than in the previous 100 years combined.The world’s first\u2026 https:\/\/t.co\/GMDgLeQzQuFebruary 13, 2026<\/a><\/p>\n

While data centers are hungry for AI GPUs and some startups are using multi-gaming GPU setups to train AI models<\/a>, they\u2019re not ideal for personal agentic AI run locally. This is especially true if you use a huge 70-billion parameter model in FP16 for your agent, which would require around 140GB of memory just for weights, according to AI investor Ben Pouladian<\/a>. That means that it wouldn\u2019t fit inside a single RTX 5090 with 32GB of VRAM, and even if you manage to connect five graphics cards for a total of 160GB of memory, you\u2019re still bound by the PCIe bottleneck.<\/p>\n

Apple\u2019s Unified Memory architecture fixes that problem. Even though HBM is still way faster than the LPDDR used in Macs and MacBooks, the fact that all the processing units \u2014 CPU, GPU, and NPU \u2014 share the same memory means that they don\u2019t have to deal with PCIe bottlenecks or require technologies similar to NVLink, which is typically only found on data center class graphics cards.<\/p>\n

The world is just catching up on what we\u2019ve been doing since 2024.For the last 2 years, at Eternal AI, we\u2019ve been running clusters and clusters of Mac Studios.These Mac clusters are perfect for long-running agentic tasks and local private LLMs.Welcome home, @openclaw \ud83e\udd9e https:\/\/t.co\/LBMEeD5Cwi pic.twitter.com\/EIPn7B6rqRFebruary 13, 2026<\/a><\/p>\n

Because of this, more and more people who want to run their own local AI agent are purchasing high-memory Mac models. This isn\u2019t limited to M3 Ultra Mac Studio units with 512GB of memory. Even Mac minis and MacBook Pros with upgraded memory now have a waiting time of two to three weeks.<\/p>\n

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We cannot definitively say that these delays are caused entirely by huge numbers of people buying these devices to run their own AI models, as Apple CEO Tim Cook admitted that it\u2019s chasing memory supply<\/a> to meet high customer demand. However, additional pressure from the consumer side will definitely not help with the memory chip shortage that, as of the moment, is primarily driven by AI hyperscalers and institutional buyers.<\/p>\n

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