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DecideAI launches its GPT-2 LLM on Internet Computer

Artificial intelligence-focused startup DecideAI has reached a key milestone with the full on-chain implementation of its large language model GPT-2.

On Aug. 28, DecideAI announced that its GPT-2 LLM had been deployed on the Internet Computer (ICP), a step that brings a decentralized approach to the training, maintenance and deployment of AI. Decentralized AI eliminates the pitfalls that beleaguer centralized AI development, the startup noted in the press release shared with crypto.news

Why the Internet Computer?

Launched by the DFINITY Foundation, the Internet Computer is a blockchain and web3 platform that enables developers to build and run decentralized applications entirely on the blockchain. This approach eliminates the need for centralized IT infrastructure, such as cloud computing services from major tech providers.

DecideAI is leveraging this decentralized ecosystem to democratize access to AI model training and deployment.

Key features of the Internet Computer include a virtual machine that uses WebAssembly to support floating-point numbers and deterministic SIMD, enabling parallel computation. DecideAI will also take advantage of ICP-native AI optimization.

“Today, the Internet Computer is the only blockchain capable of running a major LLM fully on-chain, offering the sufficient memory and compute power required to sustain a 100% decentralized AI.”

Raheel Govindji, CEO and founder of DecideAI.

DecideAI to expand Ethereum and Solana

Notably, the launch of the GPT-2 LLM on ICP opens up AI models to the benefits of interoperability, transparency and blockchain security. In the future, DecideAI will also leverage Internet Computer’s Chain Fusion technology, which allows for protocol-level blockchain integration, to expand its AI models to Ethereum (ETH) and Solana (SOL).

DecideAI’s GPT-2 large language model implementation on the Internet Computer will be powered by the web3 startup’s native token DCD.

This article first appeared at crypto.news

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