Apple Declares War on Nvidia How M5 Chip Shakes Up AI Landscape

Apple unveils the M5 chip and brands it a giant leap ahead in on-device AI and graphics performance. Apple says the M5 delivers more than four times peak GPU compute for AI compared to the M4, adds a Neural Accelerator on every core and doubles unified memory bandwidth, developments Apple terms a “next big leap” for Apple silicon.Apple unveils the M5 in new hardware: a new 14-inch MacBook Pro, refreshed iPad Pro models and a new Vision Pro that all get a boost from speed and new AI capabilities. The releases emphasize faster graphics, improved AI performance and more power efficiency. (Reuters).Benchmarks and initial data show astounding GPU and neural gains compared to M4-class chips. Independent metric websites are showing gigantic jumps in GPU Metal scores and AI throughputs, and Apple’s promotional messaging is claiming gigantic proportionate jumps in peak GPU compute for AI. Those claims now are fueling industry speculation regarding whether Apple is attempting to steal some of Nvidia’s workloads, at least on-device and in edge cases. (Notebookcheck).Why the language matters: Apple frames M5 as a computer that accelerates “demanding workflows” for developers and creatives, and professionals, but there’s a less explicit undertone. Faster neural computing and optimized GPU cycles change where some AI work gets done: on local computers instead of cloud accelerators. That shift could siphon off some of the demand for data-center GPUs, the territory where Nvidia dominates. Nvidia’s domain is high-scale datacenter training and inference huge clusters of GPUs powering server-side models. Apple’s M5 isn’t a datacenter GPU. It’s a highly power-efficient, highly integrated chip for user devices. The “war” metaphors arise because Apple is being tough to eliminate the performance gap between cloud compute and on-device compute for certain AI use cases: video and image processing, real-time conversational features, and privacy-preserving inference that is augmented with local execution.

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