This AI Model Runs on 280,000 Smartphones Instead of a Data Center — and Acurast Says Each Decision Is Verifiable

A decentralized network of Android smartphones is now running a working artificial intelligence model, and its maker says the experiment proves that real-time AI decisions do not need a data center, a GPU cluster, or an API key. Acurast, the decentralized physical infrastructure network built on smartphone compute, has deployed Laya, an open source decision model developed by Convai Innovations, across its network of more than 280,000 devices in over 175 countries.

The deployment is deliberately unglamorous and that is the point. Laya does not write essays or hold conversations. It makes structured decisions: given a defined state and a set of possible actions, it evaluates the input and picks one. Acurast is using it for classification, gaming, navigation, security and content moderation workloads, the kind of small, constant, latency-sensitive decisions that applications currently send to centralized cloud providers and pay per-request pricing for.

According to Acurast, decisions typically complete in 0.2 to 1 second and are processed on smartphone CPUs, with no dedicated GPUs involved. Work is distributed dynamically between participating devices, results come with cryptographic proof, and the phone owners are paid in ACU, the network’s native token, for contributing processing capacity.

## A different kind of model

Laya’s architecture is what makes it viable on a phone. It is non-autoregressive, meaning it does not generate output token by token the way large language models do. Convai Innovations describes it as a “System 1” decision model, borrowing the psychology shorthand for fast, instinctive reasoning rather than slow deliberative thought. The public repository lists three decision formats: choosing between defined options, scoring against a set range, and boolean yes-or-no calls.

The model family is small by frontier standards. Its English checkpoint is based on ModernBERT large at 421 million parameters, a multilingual version runs 322 million parameters across more than 100 languages, and a separate checkpoint handles typed decision workflows like customer service triage, invoice processing and security incident classification. Everything, code and weights, ships under an Apache 2.0 license, so developers can deploy it themselves rather than rent it.

“By running Laya on Acurast, we’ve changed that,” founder Alessandro De Carli said, referring to centralized cloud lock-in. “We are proving that System 1 AI can run securely and at scale on hardware everyone already owns, providing a truly open, decentralized alternative.”

## What it actually does

The live demonstrations read like a benchmark suite from an alternate universe. Laya plays Snake, Tetris and Doom, choosing movements, aiming and item collection in real time. Closer to production use, it classifies emails as inbox, spam or phishing, sorts headlines into news, satire, clickbait or manipulation, flags prompt injection attempts aimed at AI assistants, and identifies toxic messages in live chat. Users can submit their own inputs and watch the model’s decisions.

The developers are candid about limits. The repository notes the general model performs poorly on some zero-shot typed decision tests, recommends specializing it for specific tasks, advises keeping choice questions under roughly 20 options, and suggests recalibrating probabilities with application-specific data.

## Why run AI on phones

The economic thesis is that a huge share of AI workloads are not frontier-model work at all. Routing, moderation and classification run continuously, and sending every request to a large generative model hosted by a hyperscaler is expensive in both money and latency. Small decision models on commodity hardware near the user can absorb that traffic at a fraction of the cost, and Acurast’s pitch is that the commodity hardware already exists in billions of pockets.

The security model leans on Trusted Execution Environments built into modern smartphones to isolate workloads while computation happens, and cryptographic proofs accompany results so requesters can verify what ran. The network has grown quickly: Acurast raised 5.4 million USD in May 2025 when the network counted 72,000 smartphones and 256 million processed transactions. It now claims more than 280,000 devices and over 918 million on-chain transactions processed.

The deployment also slots into a broader pattern of consumer hardware being recruited for decentralized AI. Gaia introduced an AI smartphone in September 2025 designed to run models locally while contributing compute to its own network. The difference is that Acurast’s approach requires no new device, only the Android phone someone already owns.

For now, the market context is a wash. The network’s ambitions unfold against a quiet tape, with Bitcoin near 83,073 USD, Ethereum at 2,668 USD and Solana at 118 USD, per CoinGecko at the time of writing. But the longer-term question Acurast is asking deserves attention: if the marginal cost of a verified AI decision falls to the price of a phone’s idle CPU cycle, the business models built on renting decisions by the thousand begin to look fragile. De Carli’s summary is the whole pitch: decision-oriented AI, “cheaply, verifiably, and without a data center in sight.”