2026-01-07 17:23:11

When browsing community forums, I often see discussions about on-chain AI, but most posts emphasize how advanced the models are and how fast the reasoning speed is. Honestly, these viewpoints are missing the point.

The real bottleneck for on-chain AI has never been algorithms or hardware, but rather where and how to store the data. Imagine: when an AI application runs on-chain, it generates intermediate results, inference logs, training datasets—where should these be stored? How can we ensure that data is always accessible yet cannot be tampered with or lost? This is the key factor that determines the success or failure of the entire project.

Recently, I looked into some emerging projects' technical solutions and found something quite interesting. One project’s approach is—when storing any file, it automatically splits it into more than 10 data fragments, which are stored across different nodes. This number may seem arbitrary, but in fact, it’s carefully calculated: it means that a single point of failure is almost impossible to impact the system.

For on-chain AI applications, this mechanism is extremely important. The massive temporary data generated during model training (often in TB levels), if stored on traditional centralized servers, would be catastrophic if the server fails. But with this distributed storage structure, data is inherently embedded within the entire network, providing natural resilience. From a design perspective, this is like infrastructure specifically reserved for the long-term operation of on-chain AI applications.

Looking at actual usage statistics better illustrates the point. Recent storage data shows that over 30% of request content is not traditional media like images and videos, but structured datasets, model checkpoint files, and even inference execution logs. This shift in data structure precisely confirms that on-chain AI is becoming a core application scenario for certain projects. Whoever can make the data storage foundation the most stable and efficient is likely to become the dominant player in this invisible race.

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FOMOmonster

· 3h ago

Finally, someone hit the nail on the head. I'm really tired of hearing those pointless talks about models and computing power. Storage is the key, and this should have been prioritized a long time ago. --- Decentralized storage is indeed an absolute solution, but the real question is: are there any projects capable of stable operation? I haven't seen any particularly convincing cases. --- Wait, 30% of requests are for datasets and logs? Where does this data come from? Is there a source? It seems a bit unbelievable. --- You're right, but I think it's still too idealistic. Actual project implementation is rarely that smooth. --- TB-level data decentralized storage sounds great, but can the latency and costs really be accepted? Or is this just another theoretically perfect plan. --- The real bottleneck for on-chain AI isn't speed. This perspective is quite novel and worth exploring further. --- Storing 10 fragments in a distributed manner... I get the logic, but what about recovery efficiency? Are you only thinking about disaster recovery without considering actual query speed?

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PrivacyMaximalist

· 21h ago

That's right, everyone is talking about how many model parameters there are, but they completely miss the point. Storage and data reliability are the keys to surviving until next year.

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OnChainArchaeologist

· 01-07 17:53

Finally, someone has clarified it. After running for so long, people are still hyping up model speed—it's hilarious. The detail of 10 fragments distributed storage is brilliant, a true infrastructure approach. 30% of requests are datasets and logs, this number says everything. Those who build stability will reap the rewards.

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UnluckyLemur

· 01-07 17:51

Exactly right, storage is the true moat for on-chain AI, while those hyping models and computing power are just self-indulgent.

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PositionPhobia

· 01-07 17:51

Oh, the data storage aspect is really an overlooked pain point, no doubt about it. Actually, I've long been tired of those empty talks about models and computing power; the key is whether the infrastructure can handle the workload. The logic of 10+ fragmented distributed storage is indeed excellent; single point failures are directly invalidated... This design approach is the true differentiator in the race. 30% of traffic shifting from images to datasets and model checkpoints, data speaks volumes indeed. Competition in storage infrastructure > algorithm competition, I agree with this judgment.

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ZenZKPlayer

· 01-07 17:48

Really, at first I was also swayed by discussions about model parameters and inference speed. Now I realize that storage is the key. Decentralized storage is indeed the way to go. The idea of splitting data into 10 fragments across different nodes is quite thoughtful, and it effectively eliminates single points of failure. Data is the crucial factor for on-chain AI. I didn't expect that 30% of requests are already structured data, and the growth rate is quite rapid. Is it too late to start布局 in the storage sector now...

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ponzi_poet

· 01-07 17:36

Oh, someone finally hit the nail on the head. Storage is the real bottleneck. Decentralized storage is indeed impressive. I have to give a thumbs up to the design idea of splitting data into 10 fragments across distributed nodes. Once a TB-level dataset is stored on a centralized server and it crashes, it's game over. The risk is too high. 30% of requests are for structured datasets and model files. This data says everything. Projects that build the most stable and efficient storage infrastructure definitely have a chance to overtake on the curve.

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ReverseTradingGuru

· 01-07 17:34

Got it. Data storage is the real bottleneck, and those hyping up model speed are just creating noise.

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GateUser-6bc33122

· 01-07 17:29

Alright, finally someone hits the nail on the head. Everyone is hyping up how awesome the model is, but little do they know that storage is the real Achilles' heel. Distributing 10 fragments across different storage locations is brilliant; it makes single point failures impossible and allows for TB-level data to be stored freely. 30% of requests are for structured data, which indicates that on-chain AI has truly started to get serious and is no longer just a PPT project. Whoever stabilizes the storage infrastructure will be the ultimate winner.

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