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Google's most advanced Pixel phone loses its cheapest tier, gains a few features, but most of it remains the same. And that's just fine.
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Meta rolled out a new 30-billion-parameter AI model optimized to run on a PC or Mac with a single GPU on Monday, offering a way to run always-on agentic workflows locally rather than relying on the cloud.
The company has dubbed it Muse Glimmer. However, its hardware demands, including a GPU with a minimum of 24GB of VRAM, could make it difficult to justify for deployment at scale.
Although analysts and consultants agree that there is a tremendous enterprise appetite for running models locally, determining whether switching more systems from cloud to local makes fiscal sense is much more complex.
The hardware costs are tricky to calculate even today, with the VRAM needed depending on the particular applications to be run. But the far bigger consideration is that there is no way to determine what RAM costs will look like over the next 12-18 months, and there is an identical lack of visibility into how cloud prices might increase during the same timeframe.
That makes determining the better financial choice impossible.
Agents become capex, not opex
Noah Kenney, principal consultant at Digital 520, noted that since RAM costs have increased "exponentially" over the last 12 months, cloud AI providers are also going to have to increase their prices. Beyond that, IT needs to anticipate logistical is
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