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Soumya

Stealth • 2m

💡An Idea to Change the Game for AI Startups: Making AI Processing Faster, Cheaper, and Effortless Running AI models like ChatGPT, DALL·E, or AlphaCode is a computing monster—they need massive power to function, which makes them expensive to operate. Imagine paying for thousands of GPUs to generate a single conversation or image! As more people use them, the costs grow quickly. For small startups, these expenses can be a big hurdle. The Problem: Huge Compute Costs: Running these models requires powerful hardware, which is expensive. Storage & Data: Training and maintaining these models requires a lot of storage, which adds to the overall cost. Ongoing Expenses: Regular updates and fine-tuning demand continuous computing power. the solution in written below cmt👇🏻

2 replies4 likes

Replies (2)

Soumya

Stealth • 2m

The Solution: What if we could share the load? Think of it like cryptocurrency mining, but instead of mining coins, we’re using spare computing power to run AI models. By spreading the processing across a global network of unused resources, we can cut the costs and time it takes to run these heavy models. Why People Will Use It: Startups and developers can tap into affordable AI power without breaking the bank. Just like miners contribute to verifying blockchain transactions, anyone can lend their idle computing power to handle AI tasks. In return, users get faster results at a fraction of the cost! Mining Analogy: Just like crypto mining, where people lend computing power to validate transactions and earn rewards, our platform lets people share their unused computing resources to run AI tasks. Instead of earning coins, they’re helping to run AI models faster and cheaper. This decentralized power brings efficiency and affordability to AI, so anyone can use it—without the huge cost.

1 replies

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Yash Barnwal

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Which technology, semiconductor or quantum computing, is expected to have a greater impact on AI development in the future?

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OpenAI has for months been working with Broadcom to create an AI chip for running models, which could arrive as soon as 2026, reports Reuters. Meanwhile, OpenAI plans to use AMD chips through Microsoft's Azure cloud platform for model training.

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