X · by Oluwaphilemon1
Local Laya against cloud Jev, on latency
About 45 ms deciding locally against about 300 ms in the cloud.
FHILY👑
@Oluwaphilemon1
Laya is making a pretty strong case for local AI agents. On a 16GB MacBook Air, local Laya is reportedly making decisions in around 45ms. Cloud-based Jev is closer to 300ms per decision. That puts Laya roughly 7 to 11x faster in this Tetris setup. And the hardware is just a MacBook Air. No dedicated GPU server. No API round trips. No per-token bill. Laya runs locally, so once the model is on the machine, the inference cost is effectively $0 per decision. Jev has the opposite tradeoff. You get a cloud model, but every decision depends on the network and the API response coming back. Tetris makes this painfully obvious. The agent has to constantly: see the board → decide → act → see the new state → decide again. When every move takes hundreds of milliseconds, the delay compounds. At 45ms, Laya can keep that loop moving much faster. And there’s another piece I find important: Laya’s weights are open-source. That means you’re not just renting access to an AI agent through an API. You can actually run the model yourself, inspect the setup, and build around it locally. For benchmarks, you can argue about accuracy and scores all day. But put two agents inside a real-time environment and latency becomes impossible to ignore. Same Tetris board. One model waits on the cloud. The other is sitting on your laptop making decisions almost immediately. In this particular test, the difference is pretty brutal. Local inference isn’t just about saving API costs. Sometimes the biggest advantage is simply that the model is already there when you need it.
Sep 23, 2026 · 4 likesOpen on X
Oluwaphilemon1 measured local Laya deciding in about 45 ms on a 16GB MacBook Air, against cloud Jev at about 300 ms. The figures are the author’s own.
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