I/ONX
§ A — Company

Re-engineering the economics of AI at scale.

I/ONX designs heterogeneous AI infrastructure that reshapes how enterprises deploy and scale AI. Vendor-neutral by principle, single-node by architecture, and sustainable by design — built to protect budgets, supply chains, and the environment while unlocking production-scale AI.

§ A.1 — Why we exist

The problem we exist to solve

Legacy AI clusters spend more power feeding their accelerators than computing with them. That overhead — the Host Tax — makes production AI expensive, power-hungry, and locked to a single vendor's roadmap.

Our approach

Collapse the cluster into one vendor-neutral node. Compose CPUs, GPUs, ASICs, and FPGAs behind a unified memory fabric with zero-hop latency. Dramatically lower power, simpler operations, and higher utilization — without rewriting the workload.

The team

A distributed team across the United States, headquartered in Las Vegas, Nevada — engineers and operators united by one goal: production-scale AI that is efficient, sustainable, and free of vendor lock-in.

§ A.2 — What guides usFive commitments

Five values, every decision.

01

Compassion

We build to protect people, budgets, and the environment — technology in service of something larger than itself.

02

Curiosity

We question the defaults of AI infrastructure and re-engineer what everyone else accepts as fixed cost.

03

Courage

We take on the hard architectural problems others route around, because that's where the real efficiency lives.

04

Communication

We speak plainly about tradeoffs, numbers, and what our systems will and won't do.

05

Commitment

We stand behind every deployment — from first benchmark to production scale.

§ 08 — Mission

Eliminate waste.Unlock AI at scale.

By dramatically lowering power consumption, simplifying operations, and maximizing utilization, I/ONX enables enterprises to achieve production-scale AI with greater efficiency, faster ROI, and sustainable performance.

§ 09 — Contact

Ready to rethink your AI infrastructure?

Tell us about your inference and fine-tuning workloads. We'll show you what I/ONX efficiency looks like on your deployment.

Let's Talk