Panthalia
Panthalia is the verification layer that enables low-cost peer-to-peer compute to outcompete and scale.
We believe the first superintelligence will be the child of a globally distributed compute network. History has shown that scaling compute is the most effective way to scale intelligence. Achieving AGI will demand an immense amount of compute, far beyond what a few centralized providers can supply.
As compute costs soar into the trillions and the path to AGI draws nearer, the fragmented energy market will further disperse compute resources—preventing any single entity from monopolizing it.
Panthalia harnesses compute on a global scale, creating an open marketplace for verifiable compute.
Why use Panthalia?
- If you're a compute provider: You can sell your compute for more money than on other compute marketplaces—because Panthalia allows you to prove your compute is real.
- If you're looking for compute: Panthalia is more affordable than cloud, like other P2P marketplaces, but you don't sacrifice reliability.
- If you're a P2P compute marketplace: Panthalia allows you to improve prices for both buyers and sellers by eliminating fake/unreliable compute.
- If you're a researcher: Panthalia is as reliable as the cloud but more affordable and much simpler. It's very easy to define an adapter for your ML models then train without managing infrastructure. Iterate quickly.
Trust is more important than you think
Existing peer-to-peer compute marketplaces are infested with unreliable and fake compute. Despite this, existing web3 compute marketplaces have ignored verification entirely. Web2 marketplaces have used flawed reputation systems. Someone spoofing 400k fake GPU workers on io.net was inevitable.
P2P compute has been synonymous with unreliable, underclocked hardware. That’s why utilization rates for existing P2P networks have been abysmal. Researchers and companies have stuck to the cloud.
Panthalia changes this.
Panthalia uses optimistic verification to ensure the validity of compute. When users sell compute on Panthalia, they stake tokens. If someone disputes the result of their computations, a voting system determines whether they lose the stake.
Bad actors can still attempt to fake compute (and can succeed), but it is always negative expected value due to the punishment when caught.
Simple
Give Panthalia a model definition in PyTorch. Panthalia will train/inference it for you on a distributed cluster. The details are abstracted away.
No need to spend time setting up the infrastructure. No need to hire an engineer to do it for you. Simply define your groundbreaking model in PyTorch and let the platform take care of everything else.
Panthalia helps reduce research iteration time to maximize productivity.
Interested in using Panthalia to train/inference ML models? Contact us at contact@panthalia.com