Polyhedra Boosts zkML with Expander: 9000 zk Proofs Per Second

Polyhedra has released a paradigm shift of Expander, a system used to power zero-knowledge machine learning (zkML), an important engine. The update provides CUDA 13.0 compatibility, shared memory optimization up to 1 TB/s bandwidth, and GPU-accelerated KZG commitments which lead to an incredible 9000 zk proofs per second on the m31ext3 elliptic curve.

Why Compatibility with CUDA 13.0 is Important?

The update allows faultless functionality on the more current GPU-based facility, especially in the Fiat-Shamir heuristic. This optimisation allows zkML systems to effectively turn interactive cryptographic protocols into non-interactive, making both security and performance stronger. The CUDA 13.0 compatibility equips Polyhedra with the ability to future-proof Expander and attract more customers in the industrial sector that will be willing to adopt the technology as a secure, fast, and verifiable computation system.

One TB/s Bandwidth Unlocked

Polyhedra also addressed one of the largest bottlenecks of zkML accessing memories. This optimisation demonstrates just what the combination of elliptic curve cryptography (ECC) and GPU acceleration can achieve in proving times. SNARKs and other zero-knowledge proof systems are based on KZG polynomial commitments, which however usually encounter computational bottlenecks.

Backbone of zkML

All of these upgrades combine to bring a backbone to zkML that is not only faster but more robust when it comes to the real world. The partnership announced by polyhedra with Berkeley RDI is evidence of the academic and industrial traction of production-ready zkML applications. As the state of GPU acceleration, polynomial commitments, and cryptographic optimization advances, zkML is becoming a mainstream method of secure verification of AI.