Inference OS for the AI Era
To close the gap between AI's promise and what infrastructure can actually deliver — for the best cost and speed at any moment.
Inference is not a solved problem. It is becoming the defining infrastructure challenge of the decade.
Models are growing larger. Agentic systems generate 5–15× more compute demand than traditional chat. New architectures arrive faster than infrastructure can absorb them. Hardware is fragmenting — more accelerators, more programming models, more combinations that nobody has optimized yet.
And underneath all of this: nearly every enterprise already owns compute — CPUs, GPUs, edge servers, private data centers — that sits idle because the software layer to unlock it doesn't exist.
The bottleneck is not hardware. It is software.
Meanwhile, AI that depends on a single cloud provider is fragile by design. One policy change, one outage, one pricing decision can break an entire operation overnight. That is not a foundation. That is a liability.
We are building the Inference OS for the heterogeneous era.
The software layer that closes the gap between what AI can do and what infrastructure can actually deliver. Storage had its OS layer. Networking had one. Compute did too. Heterogeneous inference does not have one yet.
That layer will be built. It will define how AI runs at scale for the next decade. And the company that builds it will become one of the most important infrastructure companies of this generation. We intend to be that company.
Our north star: every enterprise running AI agents should be able to run them on hardware they already own, at the best possible cost and speed, without being held hostage by any single provider.
OpenInfer is the software layer that lets enterprises run AI wherever their data lives — across CPUs, GPUs, and NPUs, on private data centers, factory floors, and air-gapped facilities — without touching hyperscaler infrastructure.
Our engine disaggregates AI workloads and maps each stage to the most suitable hardware available. Compute-bound tasks route to high-throughput GPUs. Memory-bound tasks to higher-bandwidth accelerators. Edge workloads to the hardware already on site. All of this happens automatically.
No rewrites. No new hardware. No cloud dependency. Drop OpenInfer in, update your endpoint, and you are running. LangChain, Ollama, and vLLM all work out of the box. Your data never leaves your infrastructure.
We are also building Loom — our orchestration layer for heterogeneous distributed inference — purpose-built for enterprises running large models across fragmented compute at scale.
We did not start with a demo. We started with a thesis, and spent two years locking in the design partners, the IP, and the team to make it inevitable.
Kam Eshghi, who built and sold Lightbits to NVIDIA, leads enterprise. We have already moved past the technology problem. This is a commercial execution phase — and we have the team for it.
We are backed by Jeff Dean (Google Senior Fellow), Eric Schmidt (Innovation Endeavors), Gokul Rajaram, and Cota Capital — the most informed capital in AI infrastructure. They underwrote this thesis at seed. That should tell you what they see.
The team is built from engineers who have shipped distributed infrastructure at Meta, Google, IBM, Apple, and NVIDIA — people who have operated at the scale we are building toward.
Join Us
OpenInfer is for engineers who believe that AI infrastructure should be sovereign, efficient, and built to outlast any single provider's roadmap.
We are hiring across inference engineering, on-device performance, and AI model optimization.
See open roles →