#opg $OPG @OpenGradient I've noticed something every time I read the docs of AI infrastructure projects: they love to talk about their vision, but they're pretty shy when it comes to numbers.
OpenGradient is no different.
The term "web2-like latency" sounds super enticing. A decentralized AI network that still operates with speed akin to centralized services is what everyone’s after. The architecture that separates execution from verification also shows that the project has a practical approach instead of just trying to shove everything onto the blockchain. But the more I read, the more I realize there's a crucial piece missing: benchmarks.
How long does average inference take? How many requests can a node handle per second? What’s the cost per model call? If they're aiming to compete with traditional AI infrastructure or become the backbone for OpenGradient Chat, these are metrics that definitely need to be public.
Maybe the team is still optimizing the system before they drop the numbers, which is totally understandable. But in a field where performance dictates user experience, promises about speed would be way more convincing if backed up with specific metrics.
I still appreciate the direction OpenGradient is headed, but I also believe that transparency regarding benchmarks will be the next step to turn trust into verification.
OpenGradient is no different.
The term "web2-like latency" sounds super enticing. A decentralized AI network that still operates with speed akin to centralized services is what everyone’s after. The architecture that separates execution from verification also shows that the project has a practical approach instead of just trying to shove everything onto the blockchain. But the more I read, the more I realize there's a crucial piece missing: benchmarks.
How long does average inference take? How many requests can a node handle per second? What’s the cost per model call? If they're aiming to compete with traditional AI infrastructure or become the backbone for OpenGradient Chat, these are metrics that definitely need to be public.
Maybe the team is still optimizing the system before they drop the numbers, which is totally understandable. But in a field where performance dictates user experience, promises about speed would be way more convincing if backed up with specific metrics.
I still appreciate the direction OpenGradient is headed, but I also believe that transparency regarding benchmarks will be the next step to turn trust into verification.