One unified stack, redefining inference from kernel to cloud
We're an innovation lab reimagining inference as a full system — from kernels to engine, to an OS-like scheduler, to orchestration — all built around the new behavior of AI demand. Three economic values drive our vision: Cost, Reliability, and Sovereignty.
The Problem
Today's inference infrastructure is not built for agents.
We're seeing fragmentation across the inference stack — multi-SLA, multi-model, heterogeneous hardware — while we're about to make a million times more inference calls, in a non-deterministic fashion.
Infrastructure today is built around overprovisioning and homogeneous operations. That causes massive inefficiency, and the gap between what AI demands and what traditional cloud delivers grows 24× over the next four years.
The solution isn't adding more silicon. It's rethinking the software infrastructure stack.
Our Vision
We organize the world's AI traffic.
For AI to be ubiquitously available, it should run where it needs to — not just in the homogeneous cloud. We see a world where inference runs on heterogeneous systems, from cloud to neoclouds to on-prem and devices, in the most efficient way, easily deployed and recoverable in microseconds.
We build every layer of the stack with that modularity in mind, so AI traffic can be oversubscribed and run where it belongs — close to data, saving power, available on unstable networks, stateful and long-lasting.
What We Build
One inference stack, top to bottom.
Kernels. Compute-node scheduling. System operations. Cross-node orchestration. Every layer built from the ground up to break away from overprovisioning.
Hardware-level execution packs heterogeneous workloads per node, system operations handle health and recovery in microseconds, and fleet-wide orchestration routes and fails over across every node.
Innovation Focus
From the gap we saw in today's inference infrastructure: four hard problems, one unified stack.
01
Non-deterministic workload scheduling
We're building an inference OS: a scheduling layer that treats non-determinism as the default, packing heterogeneous workloads onto a single server and coordinating them across a fleet. The win: hardware utilization stops being the tax everyone pays for unpredictability.
02
Foundation models for inference
We're building foundation models purpose-built for inference itself — models that observe workload behavior and continuously re-optimize execution, rather than static configs tuned once and left to rot. The win: infrastructure that gets smarter the more it runs, instead of degrading over time.
03
Heterogeneous silicon compilation
We're building automated kernel generation that targets diverse silicon — GPUs, CPUs, custom accelerators — from a single workload description, with formal verification instead of numeric spot-checks. The win: full performance on any chip, with mathematical guarantees of correctness in code no human wrote.
04
Hybrid: cloud, on-prem, device
We're building the substrate-agnostic runtime that lets inference live, run, and migrate wherever it's needed — cloud, on-prem, or edge — without re-architecting for every environment. The win: your AI runs where your data already lives, not the other way around.
This isn't a slide deck. Loom and the benchmarks below are this innovation, already running.
Today's Performance Benchmarks
Same hardware. 2.5–4× the throughput. Zero dropped requests.
Same four models, same four GPUs. Once pinned one model per card, once pooled by OpenInfer across every card.
Sellable throughput
delivered inside the latency SLA
GPU utilization
peak fleet utilization, same four cards
p95 latency roughly halved on the larger models (Qwen3.5-27B: 508ms → 268ms), and every model's rejection rate fell from double digits to zero.
Read the full benchmark →Why It Matters
Three economic values: Cost, Reliability, Sovereignty.
Efficiency
In some deployments, cost drops to as little as a tenth of the original — alongside lower engineering and maintenance overhead, and a faster time to market.
Zero dropped requests, sessions run anywhere
Stateful and built for real, unstable networks, not just clean data centers. Sessions and their KV cache move to any node in seconds — no lost context, no rebuild.
Your rules, your infra
Your models and data stay wherever you deploy: cloud, neocloud, on-prem, or edge. Never locked to one.
The OpenInfer Control Tower
Every layer, one control tower.
Connect your own hardware, load models smartly, and route every inference request to the right node, live.
Then turn it into your own inference business: connect a domain, accept user signups, issue API keys, and count tokens — all through a fully customizable, white-labeled experience.
A few hours of setup. Not months.
OpenInfer Cloud
Don't own the hardware? Use ours.
OpenInfer Cloud is the same platform, run by us on our Weave-managed fleet — a hosted, OpenAI-compatible API you can call today, no silicon required. An on-ramp for teams without hardware, and proof the platform runs in production.
Who We Are
Two years building the team and the IP before writing a line of marketing copy. Our team has shipped distributed infrastructure at some of the largest technology companies in the industry. Our head of enterprise previously built and sold his last company to a major technology company. With design partners and pilots across the industry, we've already deployed over a trillion tokens in production.
Jeff Dean (Google Senior Fellow), Eric Schmidt's Fund, Gokul Rajaram, and Cota Capital — the most informed capital in AI infrastructure.
Join Us
Help build the OS for the agentic era.
We're hiring across inference engineering, system performance, and AI model optimization.
See open roles →