New Airdrop Alert
The day after tomorrow, I saw the analysis from the plaza influencers saying it needs 250 points
It's crazy, just rolling with the punches, if it happens, it happens, if not, whatever
Not long ago, I wanted to run a larger open-source model for data processing, but my local 3060 GPU couldn't handle it. I thought about finding a decentralized AI network to shift the inference over. Tried three different protocols, but either they only supported a few fixed models or required tweaking a bunch of parameters. Initially, I thought choosing a computing power platform would do the trick, but it took me two backend switches to realize: DeAI still lacks a universal inference access standard.
That's when I started re-examining OpenGradient's roadmap and architecture.
Many people view it as just another distributed computing node network, but to me, it seems more like an attempt at a universal access base for AI inference. What OpenGradient aims to achieve isn't just adding more GPU nodes to call upon but enabling different models and applications to share the same standards for scheduling, running, and on-chain verification.
The same access request can accommodate models of varying sizes, coordinate computing power across nodes, and even choose different levels of verification schemes. For users, it means no need to repeatedly adapt model formats across different protocols; for developers, it means no need to write separate integration logic for each computing network. It may seem like just a standardization at the interface level, but it directly influences the growth efficiency of the entire DeAI ecosystem.

As I followed its access route, I had a clear feeling: having one more application protocol natively supported broadens the coverage; the wider the coverage, the more nodes and model providers will be attracted to connect. This isn't just a one-time node expansion; it's a continuously self-reinforcing cycle.
Many projects are competing for single-card computing power and pushing for lower inference prices, but prices fluctuate, and subsidies dwindle. The real challenge is becoming the default access choice for developers.
After completely mapping out the logical route of @OpenGradient , I feel that their relentless pursuit of a universal inference access standard has more long-term potential than projects that merely focus on computing power scale or inference pricing. $OPG , I'm not going to blindly jump in, but I will add #OPG to my long-term watchlist to track it slowly. $EVAA $CLO