Just saw a hot topic on Twitter, NVIDIA's Inception plan is backing the decentralized AI space again, and the project getting all the hype from major KOLs is OpenGradient, which just hit Binance last month. The official marketing slogan is making a lot of noise, claiming it's a dedicated network built for open intelligence. To put it simply, they just want to forcefully stitch together the decentralization of Web3 and AI inference. #ETH
OpenGradient's privacy protection architecture is definitely a huge innovation, a must-have for privacy enthusiasts. But, whoa, let’s not get too excited too soon. When I actually switched to the Alpha testnet to try triggering on-chain model inference via smart contracts, the issues came pouring in. @OpenGradient
The so-called 'web2 level low latency' touted by OpenGradient works fine when running lightweight risk models with a few million parameters, but once I tried to call a larger language model and required generating complete cryptographic proofs, the latency was downright frustrating; sometimes it would just throw a timeout error. Ugh, seriously, this is far from 'scalable capacity', and the current computing efficiency and bandwidth loss have cut its practicality by more than half. #OPG
This brings us to a clear discussion about OpenGradient's secondary market performance and future development. The OPG token from OpenGradient just hit Binance at the end of last month, spiking at the open but has since dropped about 34% over the last month, currently oscillating around $0.16. From the tokenomics perspective, with a total supply of 1 billion OPG and only 190 million currently in circulation, it means over 80% of $OPG tokens are still waiting to be unlocked. This natural sell pressure is no joke. #BTC
If OpenGradient ends up being just a flashy 'concept packaging', and can't genuinely resolve the performance bottlenecks of multi-node parallel inference in the upcoming mainnet iterations, then $OPG will likely just drift along with the market. At this stage, I really wouldn't recommend blindly buying at high positions; it would be wiser to use the 1,000 free credits given by the official to try out the main model in OpenGradient Chat, or continue to monitor their tech updates on the testnet. That's the most sensible strategy.
OpenGradient's privacy protection architecture is definitely a huge innovation, a must-have for privacy enthusiasts. But, whoa, let’s not get too excited too soon. When I actually switched to the Alpha testnet to try triggering on-chain model inference via smart contracts, the issues came pouring in. @OpenGradient
The so-called 'web2 level low latency' touted by OpenGradient works fine when running lightweight risk models with a few million parameters, but once I tried to call a larger language model and required generating complete cryptographic proofs, the latency was downright frustrating; sometimes it would just throw a timeout error. Ugh, seriously, this is far from 'scalable capacity', and the current computing efficiency and bandwidth loss have cut its practicality by more than half. #OPG
This brings us to a clear discussion about OpenGradient's secondary market performance and future development. The OPG token from OpenGradient just hit Binance at the end of last month, spiking at the open but has since dropped about 34% over the last month, currently oscillating around $0.16. From the tokenomics perspective, with a total supply of 1 billion OPG and only 190 million currently in circulation, it means over 80% of $OPG tokens are still waiting to be unlocked. This natural sell pressure is no joke. #BTC
If OpenGradient ends up being just a flashy 'concept packaging', and can't genuinely resolve the performance bottlenecks of multi-node parallel inference in the upcoming mainnet iterations, then $OPG will likely just drift along with the market. At this stage, I really wouldn't recommend blindly buying at high positions; it would be wiser to use the 1,000 free credits given by the official to try out the main model in OpenGradient Chat, or continue to monitor their tech updates on the testnet. That's the most sensible strategy.