In the rapidly evolving landscape of artificial intelligence and automation, a transformative wave is poised to reshape industries and economies. The challenge lies in either integrating these advancements into existing resources or becoming reliant on cloud giants. The high costs associated with AI inference have prompted a search for innovative solutions, particularly the underutilized GPU resources from mining operations, which could potentially offset these expenses.
Concept: P2P Infrastructure Based on MoE
The “AI Torrent” represents a decentralized P2P network designed for AI model inference, founded on three pivotal principles:
- BitTorrent Economy. Nodes within the network, ranging from personal PCs with GPUs to servers, exchange computational resources on an equitable basis. By sharing GPU resources, users gain free or prioritized access; those who do not share incur costs. Revenue is directed to those who contribute resources.
- Smart Swarm Architecture (Mixture of Experts). Instead of deploying a singular, massive model, the network comprises numerous specialized “experts” (layers or sub-models, each 1–5 GB). A decentralized router dynamically allocates request fragments across the network for parallel execution.
- Self-Organizing Intelligence. Popular models organically migrate to the most active nodes. Combo-experts from various repositories, such as HuggingFace, can merge, resulting in emergent properties and robust hybrid AIs without dependence on cloud monopolies.
How It Works for Different Participants
For Users Without Computational Resources
Interaction is facilitated through a standard API or chat interface. After installing the client (available as an exe installer for Windows, a similar version for macOS/Linux, or a mobile app) and connecting a crypto wallet (with an integrated option to purchase tokens via credit card), users can input prompts (e.g., “generate Python code for data analysis”), select a model (Llama-3, Mistral, or hybrid), and confirm a microtransaction in utility tokens. The decentralized router automatically distributes the request to 3–5 nearby nodes, returning results within 200–500 ms. Request histories are stored locally or in IPFS, ensuring anonymity without centralized accounts or subscriptions.
For Users with Computational Resources (Seeders)
The client activates a passive “mining” mode. The network scans for idle capacities (greater than 50% free) and registers the device as a node in the P2P network via the DHT protocol. When requests from other users arrive, your node receives task fragments, performs inference, and accumulates utility tokens in proportion to its contribution (measured by FLOPS or processed output tokens). A built-in dashboard displays earnings, statistics, and staking options to enhance priority, enabling passive income without manual intervention.
For AI Model Creators
Developers can upload models (in HuggingFace format) through an API or web interface to a decentralized storage system (IPFS with torrent-like seeding). The network automatically splits the model into “experts” (layers) for MoE routing, benchmarks performance, and assigns specialization labels. Royalties (5–10% of each request’s cost) are automatically distributed via smart contracts. A dashboard provides analytics on usage and earnings, allowing creators to monetize their developments directly, without intermediaries.
Economics: Tokenized Exchange Model
The economy of the “AI Torrent” is built on utility tokens (AIT), whose value is linked to the market price of computations and is expected to decrease as the network expands, aiming to be significantly cheaper than centralized alternatives.
- Revenue Distribution: 70% to seeders, 20% to model developers (royalties), and 10% to a DAO fund for protocol development.
- Liquidity and Stability: Tokens are traded on DEX platforms (e.g., Uniswap), with volatility minimized through staking (providing network priority) and partial backing with reserves in stablecoins (USDC).
- Network Growth: The DAO may subsidize “public” nodes for research or free tier limits, accelerating user acquisition in the early stages.
Analogues: Existing Projects in Decentralized AI
The concept is not entirely new, as several projects have already demonstrated the viability of P2P inference. The “AI Torrent” aims to harness the best features of these existing solutions.
| Project | Description | Similarities to “AI Torrent” | Differences |
| Petals | P2P network for distributed inference LLM, where the model is divided into layers, each on a home PC. | Torrent-like layer exchange, sharding, focus on idle resources. | No built-in economy; more for enthusiasts. |
| Bittensor (TAO) | A decentralized ML marketplace with 32+ specialized subnets; nodes “mine” outputs. | MoE routing across subnets, tokenized economy, model migration based on profitability. | More complex architecture, focus on training + inference. |
| Gensyn | DePin protocol for ML-compute with blockchain verification of computations. | Resource exchange for tokens, distributed execution. | More focused on training (GPT@home) than on rapid inference. |
| Render (RNDR) | A decentralized GPU network, initially for rendering, now also for AI/ML. | Idle GPUs as nodes, tokenization (RNDR for FLOPS). | Historical focus on graphics, more centralized routing. |
These projects illustrate that decentralized AI is already a multi-billion dollar industry. Our objective is to integrate their best mechanics and concentrate on a singular task: making inference accessible, swift, and affordable for all.
Challenges: Realistic Barriers and How to Overcome Them
Decentralized systems are inherently fragile. Below are key challenges and our hypotheses for addressing them:
| Challenge | Why It Hurts | Solution in “AI Torrent” |
| Latency in P2P | Global networks can incur delays of 200–800 ms, plus an additional 50–100 ms for MoE routing. | Geo-DHT and edge caching (requests to the 3 nearest nodes). Goal: <300 ms. |
| Data Privacy | Prompts traverse foreign nodes, creating leak risks. | Zero-knowledge proofs (ZK-SNARKs) for output verification without data exposure; local tokenization and prompt processing. |
| Malicious Nodes | A single “poisoned” expert could deliver false results. | Slashing (token penalties for low accuracy); on-chain reputation (Elo rating); selective rechecking of 1% of requests. |
| Regulations (EU AI Act) | From 2025, GPAI models will require auditing and transparency. | We utilize only open-source models; the DAO ensures auto-generation of risk and bias reports. |
| Network Stability | Initially few nodes can lead to queues and slow performance. | Bootstrap grants from the DAO for the first 100,000 nodes; integration with Telegram/Discord bots for viral growth. |
These challenges serve not as barriers but as a checklist for our minimum viable product (MVP). We aim to address 80% of these issues at launch (ZK + geo-routing), with the remainder outlined in our roadmap.
