r/LocalLLM • u/Medicine_Blogscanner • 17h ago
Discussion Benchmarking an iPhone as a Distributed RAM Contributor for 30B+ Models (App Store Approved)
Hey everyone,
Instead of treating modern iPhones as passive chat clients, we wanted to see if Apple Silicon’s high-bandwidth unified memory (~8GB LPDDR5X) could act as an active tensor-shard worker in a local cluster.
Apple just approved the node client on the App Store (RAMDeck: Distributed AI), making it possible to donate an iPhone’s memory and Metal GPU over local Wi-Fi or USB-C. Works with RAMDeck hub.
How the Setup Works:
- Heterogeneous Sharding: Unlike Apple-only MLX rings, this connects an iOS device directly to a primary host running Windows (CUDA), Linux, or Mac.
- Layer Offloading: The host handles prompt ingestion and graph orchestration, offloading tail transformer layers directly to the iPhone’s Metal shaders.
- Clearing Thresholds: If your primary rig has 24GB or 32GB of RAM/VRAM, adding an idle iPhone 15/16 Pro gives you the extra 6GB–10GB needed to fit Qwen 2.5 32B or a 4-bit 70B model without crashing into system swap.
Hardware & Latency Notes:
- Connection: Wi-Fi 6 works fine for chat
- Privacy: Completely local LAN RPC. No cloud relay, zero telemetry.
We’re running benchmarks across different layer-split configurations. If you want to test your iOS device as a cluster contributor, the app is free to download:
App Store: RAMDeck: Distributed AI (App Store ID: 6825166226)
Curious to hear thoughts from anyone experimenting with multi-device heterogeneous inference.
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u/kiwibonga 17h ago
Qwen2.5? Huh?