Cysic and NOYA are rewriting the rules of scalable zkML.
If you’ve been tracking zkML adoption, one bottleneck has always loomed large: proving. AI agents like NOYA’s need to verify model outputs across multiple chains, but conventional setups cloud GPUs, general-purpose hardware choke on speed and cost, making real scalability a distant dream.
This is where @cysic_xyz steps in, turning a structural weakness into a competitive advantage. Their purpose-built proving hardware isn’t just faster it’s transformative. Capable of exceeding 1.31M keccak/sec, it reduces verification costs by roughly 91%, converting zkML proving from a cloud-billed expense into a practical, high-performance infrastructure.
The pipeline itself evolves: ONNX models → zkML → Halo2/KZG → on-chain verification now executes on specialized silicon rather than standard GPUs, removing inefficiencies at every stage. This isn’t a simple integration; it’s a full-scale upgrade to the proving stack, redefining how on-chain AI operates.
As zk workloads continue to surge, success will hinge on speed, cost, and scalability. Networks that can prove the fastest and cheapest at massive scale will dictate the next chapter of decentralized AI.
@cysic_xyz isn’t merely riding the zkML wave it’s building the engine powering it. The implications for on-chain intelligence, multi-chain AI coordination, and the broader cryptographic ecosystem are immense.
The era of scalable, efficient, hardware-accelerated zkML has officially arrived.
@cysic_xyz and @NetworkNoya

I’m genuinely excited for the moment @cysic_xyz goes live on mainnet and I hope it coincides with a broader market recovery. The timing could be perfect for ZK technology to finally step into the spotlight.
Over the last six months, the conversation around zero-knowledge proofs has mostly centered on algorithmic tweaks and cryptographic refinements. But here’s the reality: the real bottleneck isn’t the math it’s compute. Most teams are still cobbling together temporary clusters or patchwork solutions, which makes scaling fragile, costly, and unpredictable. Without reliable infrastructure, even the most elegant algorithms hit a ceiling.
What sets #Cysic apart is its supply-side approach. Rather than improvising or layering quick fixes on top of existing systems, they are reimagining the entire ZK compute pipeline. Think of it less like building a car from spare parts, and more like moving to a standardized, high-performance production line.
At the hardware level, their hybrid ASIC + GPU architecture unlocks instruction-level optimization, squeezing every ounce of efficiency from the silicon. On the network side, compute is transformed into a verifiable, on-demand marketplace tasks can be tracked, scheduled, and allocated with precision.
The outcome? What used to be a fragmented, inconsistent proving process becomes a robust, scalable, and tradable compute infrastructure. Compute is no longer a bottleneck it’s a standardized resource that ZK projects can depend on, paving the way for a new era where zero-knowledge proofs can scale predictably and sustainably.
In short, @cysic_xyz isn’t just solving a technical limitation it’s laying the foundation for #zk to grow into a mainstream, production-ready ecosystem, one compute block at a time

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