Open Source AI — August 24, 2026
No significant new developments today on this topic. The last few days already covered the big items in play — hyperscalers spending essentially all their cloud revenue on AI buildout, and a small open-weight model from Alibaba matching much bigger commercial systems.
In Plain English: No significant new developments today on this topic. The last few days already covered the big items in play — hyperscalers spending essentially all their cloud revenue on AI buildout, and a small open-weight model from Alibaba matching much bigger commercial systems. Nothing that clears the bar for genuinely new news turned up in the last day. The standing argument about Apple's hardware position below still holds and is worth a re-read even on a quiet day.
No significant new developments today on this topic.
The Apple Angle (Standing Perspective)
Worth restating on a quiet news day: the hardware maker best positioned for a shift toward running models yourself, rather than renting them by the token, may not be Nvidia at all. The case rests on Apple Silicon's unified memory, which lets a single Mac Studio — about $9,500 — be configured with up to 512GB, enough to load a trillion-parameter open-weight model on one desktop machine without splitting it across a server rack. Reaching that same capacity with Nvidia's RTX Pro 6000 workstation cards would take five or six of them, on the order of $60,000–$75,000 combined, while pulling roughly ten times the power.
The argument compounds with every open-weight release: as frontier-caliber models from labs like Alibaba, DeepSeek, GLM, and Kimi keep shipping every few weeks, the bottleneck shifts from "who has the smartest model" to "who can afford the box to run it locally." Multi-Mac clusters using MLX and Thunderbolt 5's RDMA networking can already pool memory across several Studios for even larger models, and Nvidia's own DGX Spark quietly concedes the point by adopting a unified-memory design of its own — notably after Nvidia stripped NVLink pooling out of its consumer and workstation cards. The fair caveat still stands: a Mac Studio serves one user running one model locally, while a data-center GPU cluster serves many people at once — so this looks like Apple leading a specific, valuable segment, not the whole of AI compute.
Limited Edition Jonathan (Substack) · Perspective
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