DSperse
160 million ZK proofs — trustless AI inference you can independently verify
DSperse generates cryptographic proofs that a specific AI model ran correctly.
The simplest explanation
Like a notary for AI computations — proves a model ran correctly without revealing inputs.
Everything you need to understand DSperse
No jargon. No whitepapers. Just the facts.
The Problem
AI outputs from unknown servers can't be independently verified. Businesses using AI APIs have to trust the provider ran the correct model on the correct input — there is no cryptographic receipt. In regulated industries, healthcare, and financial services, that blind trust is a liability.
What DSperse Does
DSperse generates cryptographic proofs that a specific AI model ran correctly.
The Edge
DSperse (formerly Omron) has processed 160 million+ zero-knowledge proofs — making it the world's largest decentralized ZK proving cluster. Every AI inference is accompanied by a cryptographic proof that the computation happened correctly, without revealing the underlying data. Enterprise-focused with a Rust-based Expander ZK backend for high performance.
What's the closest mainstream equivalent?
The best way to understand a new technology is to compare it to something familiar.
DSperse (formerly Omron) has processed 160 million+ zero-knowledge proofs — making it the world's largest decentralized ZK proving cluster. Every AI inference is accompanied by a cryptographic proof that the computation happened correctly, without revealing the underlying data. Enterprise-focused with a Rust-based Expander ZK backend for high performance.
How does Bittensor make this possible?
DSperseuses Bittensor's incentive layer to build something no single company could run alone.
Miners compete
Participants (called miners) on the DSperse subnet compete to produce the best outputs for zero-knowledge ai verification tasks. Anyone with the right hardware can join.
Validators score the work
Validators continuously evaluate miner outputs against objective benchmarks. The best performers rise, the worst are replaced. There's no human committee — the protocol decides.
Rewards flow to the best
Miners are paid in the DSperse alpha token in proportion to how good their work is. This creates a continuous competitive pressure that drives quality up and cost down — structurally, not just as a promise.
The whole network benefits
Because every participant is aligned toward the same goal — producing the best zero-knowledge ai verification results — the network improves continuously without requiring a central team to manage it.
Frequently Asked Questions about DSperse
What is DSperse on Bittensor?
DSperse is Subnet 2 (SN2) on the Bittensor network — a decentralized AI protocol built on the TAO blockchain. DSperse generates cryptographic proofs that a specific AI model ran correctly.
What problem does DSperse solve?
AI outputs from unknown servers can't be independently verified. Businesses using AI APIs have to trust the provider ran the correct model on the correct input — there is no cryptographic receipt. In regulated industries, healthcare, and financial services, that blind trust is a liability.
How is DSperse different from Modulus Labs, EZKL, or Proof of AI?
DSperse (formerly Omron) has processed 160 million+ zero-knowledge proofs — making it the world's largest decentralized ZK proving cluster. Every AI inference is accompanied by a cryptographic proof that the computation happened correctly, without revealing the underlying data. Enterprise-focused with a Rust-based Expander ZK backend for high performance.
What is the DSperse token?
The DSperse subnet has its own alpha token on Bittensor's dTAO system. It trades in the Bittensor liquidity pool and its price reflects market demand for the subnet's services.
Is DSperse a good investment?
AlphaGap tracks DSperse using its aGap score — a composite of development activity, token flow, and social signals. This page is for informational purposes only and is not financial advice. Always do your own research before making any investment decisions.
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