OpenInfer
OpenInfer offers an end‑to‑end inference platform that aggregates heterogeneous edge, on‑premise, and cloud hardware—CPUs, GPUs, and NPUs—into a single coordinated runtime. By automatically partitioning and load‑balancing large AI models across fragmented compute nodes, it keeps data where it resides, delivering low‑latency, sovereign inference with enterprise‑grade reliability and reduced total cost of ownership.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Funding
Raised to date
$8MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises that need to run large AI models often rely on centralized cloud servers, which requires moving sensitive data offsite, incurs high bandwidth costs, and underutilizes existing on‑premise or edge compute resources such as CPUs, GPUs, and NPUs.
Solution
OpenInfer provides an end‑to‑end inference platform that aggregates heterogeneous edge, on‑premise, and cloud hardware into a single coordinated runtime. By distributing model inference across fragmented compute nodes, the system keeps data where it resides, preserving sovereignty and reducing latency. The platform automatically orchestrates workloads, handles model partitioning, and maintains high availability through always‑on agents. It delivers enterprise‑grade reliability, security, and performance while lowering total cost of ownership by leveraging idle resources. OpenInfer’s simple deployment and unified management interface enable organizations to run large language, vision, and world models without sacrificing accuracy.
Target Audience
Primary customers are defense and intelligence agencies, industrial IoT operators, and enterprise IT departments that require secure, low‑latency AI inference on edge or on‑premise hardware.
Features
- Custom distributed inference engine that meshes CPUs, GPUs, and NPUs across edge, on‑premise, and cloud environments
- Automatic model partitioning and load balancing to utilize idle compute and achieve low‑latency inference
- Always‑on agents with resilient synchronization for mission‑critical and air‑gapped deployments
- Sovereign data handling: inference runs locally, eliminating the need to transfer raw data to external clouds
- Unified management console and API for monitoring, scaling, and integrating with existing enterprise workflows
- Support for large models (70B+ parameters) and expansion to vision and world models across diverse silicon