MLPerf Mobile vs mycellm

MLPerf Mobile is a free, open-source benchmarking tool designed to measure the performance of mobile devices like smartphones and tablets in various artificial intelligence (AI) and machine learning (ML) tasks. Workloads tested include image classification, language understanding, super resolution upscaling, and image generation based on text prompts. This benchmark makes use of hardware AI acceleration on many of the latest mobile devices to ensure the best performance where possible.

Mycellm turns your iPhone or iPad into a node on a distributed AI inference network. Run open LLMs on your own hardware — no account, no telemetry, no cloud middleman. Use it three ways, and own every layer. LOCAL — ON YOUR DEVICE Load a model and run inference entirely on-device. Fully offline, airplane-mode friendly. On-device Privacy Guard scans prompts for secrets before anything hits the wire. TRUSTED DEVICES — YOUR OWN NETWORK Point your phone at a node you trust — your Mac Studio, your iPad Pro, your homelab — and run models far bigger than any single device could hold. Your phone stays light; a machine you own does the heavy lifting. No cloud. PUBLIC — THE FREE NETWORK Tap the public mycellm net: open models served by GPUs worldwide. Earn credits by serving spare compute, spend them to run larger models. Peer-to-peer, no subscription. FEDERATE Stand up your own network with its own trust policy and credit ledger, mint invite tokens, and bridge trusted networks with cross-network routing. You decide membership, models, and data flow. UNLEASH THE iPAD PRO An iPad Pro (M4/M5) is a serious AI machine that sits idle most of the day. Put it to work: more unified memory runs larger models, Metal delivers 30+ tok/s on multi-billion-parameter models. Serve your iPhone, your team, or the network — first-class on any device that runs a model. FOR PEOPLE WHO RUN THEIR OWN STACK - Serves the same chat-completions REST API your tools already speak — point curl, Claude Code, or your own clients at your device. - Vision models: attach an image and ask, on-device. - Live dashboard: tok/s, credits, peers, node health. - Auto hardware-fit: picks quantizations that match your RAM. - Open source, Apache 2.0. Audit it, fork it, run it. No black boxes, no lock-in. It's BitTorrent for LLMs — every node both uses the network and makes it stronger. Full docs and use cases at mycellm.dev. Bring your own models, keep your data, and run AI on your terms.