iPhone 17 Pro Max

Headlines · 2

1. Oct 17
   Five devices chained to run Qwen3.8 Flash Next at 37–39 tok/s on 60k context

   r/LocalLLaMA user StayLameBro says he chained two gaming PCs (an RTX 2060 Super and an RX 9060 XT), an M4 Pro MacBook and two iPhone 17 Pro Maxes over Ethernet and USB-C to serve Qwen3.8 Flash Next, which he describes as a 48-layer mixture-of-experts model with 512 experts per layer. The self-measured decode rates are about 42 tok/s at 16k context, 39–41 tok/s at 32k and 37–39 tok/s at 60k, with prompt reading around 850–900 tok/s on long prompts; he credits pipelined speculation for keeping the five devices from idling, landing about 2.2 tokens per step. The author says the current layout serves 69.6k context, capped by the second PC's 16GB, that a second layout read a single prompt past 164k tokens, and that a full 262k layout is still pending; the code is described as coming to a repo shortly.

   r/LocalLLaMA · 🔥 0
2. Oct 13
   ModelBest open-sources MiniCPM-V 4.7 edge multimodal model

   ModelBest and OpenBMB released MiniCPM-V 4.7, an on-device multimodal model family with 35B-A3B and 1B variants; 35B-A3B is the first MoE model in the MiniCPM-V line, with 35B total parameters and about 3B activated per inference. The company says its LES (look-ahead expert scheduling) technology lets the 35B-A3B version decode at 13+ tokens/s on an iPhone 17 Pro Max using only about 3GB of memory, making it the first open-source MoE to run losslessly and smoothly on devices; Zwicky's hands-on test found second-level response times and accurate routine image tasks, though the online demo capped output length. The models support iOS, Android and HarmonyOS and Intel, AMD, Arm, Rockchip and Houmo chips, with the Self-Replay Fine-Tuning (SRFT) recipe also open-sourced; ModelBest's tests say 35B-A3B scores higher overall than Qwen3.6 35B-A3B with shorter inference time in Instruct mode.

   IT之家 AI · 🔥 0

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