Five devices over Ethernet and USB-C run one 512-expert model
A user reports 37 to 39 tok/s on Qwen3.8 Flash Next at 60k context across two PCs, one MacBook and two iPhones.
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On October 17, 2026, a user on r/LocalLLaMA chained five machines into one inference pipeline for Qwen3.8 Flash Next, a 48-layer mixture-of-experts model with 512 experts per layer. Two gaming PCs, an RTX 2060 Super and an RX 9060 XT, an M4 Pro MacBook, and two iPhone 17 Pro Maxes. The PCs and the Mac talk over 1 GbE Ethernet, the phones over 10 Gb/s USB-C. Measured at the server: 37 to 39 tok/s write at 60k context, 856 to 895 tok/s prompt read, about 0.5 s to start a cached follow-up turn.
The mechanism is pipelined speculation. A draft head on PC 1 guesses the next tokens, the chain verifies them in one pass, and PC 1 drafts the next batch while that pass travels. Each step lands about 2.2 tokens on average. Layers are split by device, with the Mac serving as expert cache for the phones. The 60k layout caps at 69.6k context on PC 2’s 16 GB; a second layout read one prompt past 164k tokens and writes 31 to 33 tok/s at 60k. The author notes the on-screen prefill counts cached prompt tokens, so it reads high. Repo is github.com/StayLameBro/backburner, not posted yet.
Source: r/LocalLLaMA, October 17, 2026.
Why it matters under load: Distributed inference over hardware already on the desk changes fleet sizing decisions, but latency, not device count, sets the throughput ceiling.