Crynux (crynux.io) (CNX)

This is exactly the kind of feature DeAI needs if it wants to onboard more than just power users.

Running AI infrastructure shouldn't require everyone to own a GPU or know how to operate a node.

That's why I like what $CNX is doing with delegated staking.

For anyone unfamiliar, @crynuxio is building a permissionless AI compute network powered by everyday hardware.

Home PCs with NVIDIA GPUs.
Apple Silicon Macs.
Cloud instances.

Instead of relying on massive centralized data centers, AI workloads like inference, fine-tuning, and image generation are distributed across a global network of edge devices.

Now, if you hold $CNX, you don't need to run hardware to participate.

You can simply:

• Connect your wallet to the Crynux Portal
• Browse available node operators
• Delegate your $CNX

The operator runs the infrastructure.

You earn a share of the rewards generated from real AI compute tasks.

It's a much lower barrier to entry.

Not everyone has an 8GB+ GPU sitting idle.

Not everyone wants to deal with setup, monitoring, or maintenance.

Delegated staking lets those users still participate in the network.

It also creates better alignment.

Node operators receive more stake backing reliable infrastructure.

Delegators earn without operational overhead.

As more AI workloads flow through the network, both benefit.

Most AI projects sell the same pitch

Spare GPUs, token rewards and "decentralized."

Nobody explains how you actually verify anonymous machines did the AI work honestly.

We asked @crynuxio that exact question on our AMA last week:

https://t.co/moRk7wIMfo

Their answer is vssML: deterministic execution + secret task sampling + ZK proofs on the sampling itself (proving the whole model isn't feasible yet).

Nodes never know which tasks get checked, so they can't selectively cheat. That consensus-first approach is what actually separates them from the $TAO / $IO crowd, not just another GPU marketplace with a token bolted on.

Their Lithium mainnet is live.

You can run a node, delegate stake to one, or plug into their OpenAI-compatible API right now. T

he long game: make smaller open-source models cheap and accessible instead of chasing OpenAI at the frontier.

In the AMA, Luke goes deep on the consensus design, why ZK proofs failed for full neural nets, how task pricing works (think gas fees, not fixed pricing), the node onboarding steps, and where they see $CNX and the network in 4-5 years.