Inference platform for neoclouds, private cloud, and enterprise

Unlock 4 to 10x more capacity from your inference infrastructure.

We virtualize AI infrastructure. VMware unlocked massive compute by letting applications share and oversubscribe one server. OpenInfer does for inference what virtualization did for compute: 4 to 10 times more customers inside the same SLO, deployed in hours instead of months.

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Buying inference capacity today means three bad deals.

01

The hardware you bought sits underutilized

Throughput stays low. You pay full price for silicon that is idle most of the day.

02

Every deployment is a project

MLOps pipelines, server configuration, a major capital commitment, and months before the first token is served.

03

The only advice you get is to buy more expensive GPUs

Same architecture, bigger invoice, and the same utilization problem waiting next year.

What changes with the OpenInfer platform.

One system installs on the fleet you already run and starts serving.

Setup

MLOps, agentified

Infrastructure comes up in hours instead of months. No engineering work is required on your side.

Capacity

4 to 10 times more from the same servers

Same silicon, same power bill, far more billable throughput inside the latency target you already promise.

Hardware

Truly heterogeneous

Keep what you own and upgrade on your own schedule. Any vendor, any generation, GPUs and CPUs pooled as one.

Footprint

Cloud and on-prem in one pool

Mix rented cloud capacity with the hardware in your own racks. This is where hybrid and heterogeneous finally unlock together.

Sellable tokens per nodeSame node. Same open source models. Same power bill.
Stock open source serving stack1.0x
OpenInfer4 to 10x
Billable capacityIdle silicon you already paid for

4 to 10 times more customers served on hardware you have already paid for.

Capacity is not a benchmark line. It is the number of customers you can serve inside the same SLO, and it lands directly on revenue per rack.

Validated, deployed, and carrying our own traffic.

Intel

3x faster inference on Intel Xeon 6 SoC

Measured and published by Intel.

Read report →

Neocloud partners

Live in production today

Serving paid traffic on partner fleets, under their own brands and their own endpoints. More than one trillion tokens served.

platform.openinfer.io

We run our own cloud on it

The software we sell is the software we depend on. Every request to our endpoint is served by this platform.

One unified stack, kernel to cloud

We build every layer between your silicon and your customer’s API call.

Bolted together pieces leak performance at every seam: a gateway, a router, a scheduler, and a serving framework that each guess at what the others are doing. OpenInfer is one system, so a routing decision knows what the kernel is doing on every card.

APIYours

OpenAI compatible API, your domain, your keys

Every request authenticated, scoped to a tenant, and metered before anything is placed.

RouterFleet wide

SLA aware routing across every server

Routes on live signals: latency against each model’s SLA, where models are loaded and warm, and what has room to serve. Sheds traffic off a drifting node before your customers feel it.

Scheduler
Engine
KernelsPer node

Server 01 8x GPU

Scheduler · Engine · Kernels
GPU
GPU
GPU
CPU

Server 02 Mixed vendor

Scheduler · Engine · Kernels
GPU
GPU
NPU
CPU

Server 03 Prior gen

Scheduler · Engine · Kernels
GPU
GPU
GPU
CPU
SiliconYours

Your GPUs, CPUs, and NPUs, whatever mix you already run

No fleet standardization, no rip and replace. Prior generation cards and idle host CPUs count as capacity, not as waste.

Model loaded, warm, and serving Headroom on the same device Requests flow API to router to node, and tokens stream back
Kernels and engine

Tuned per device, not per fleet

The runtime detects each device on boot and tunes KV cache, weights, and memory for that specific chip. No per node config to write and maintain.

Node scheduler

Many models per card

Memory is oversubscribed so more models stay resident per device than would naively fit, then repacked as demand shifts between them.

Fleet router

Failure is a routing event

Nodes stream health continuously and count as present only while connected, so a dead node drops out cleanly with no stale routing and no manual re-registration.

One click deploy

A node goes from boot to serving paid traffic in three moves.

There is no separate orchestration layer to stand up and no engineering project to staff. The runtime ships like any other workload on your fleet.

  1. 01

    Install the runtime

    Bake the OpenInfer runtime into your node image and roll it out with the tooling you already use: Kubernetes, Ansible, CDK, an autoscaler, or bare metal.

  2. 02

    The node connects out

    On boot, each node opens one outbound connection to the control plane and reports what it can serve. No public IPs, no inbound ports, no manual registration.

  3. 03

    Models pack and go live

    The node receives its packing plan, loads and warms the models across its GPUs and CPUs, and starts serving on your endpoint. Nodes join and leave as you scale them.

Where the demand is coming from

Enterprises are pulling inference back onto hardware they control.

Every one of those workloads has to land somewhere. It lands with whoever can offer control, a defensible cost per token, and a running endpoint this quarter.

Control

Their models, their versions, their SLAs

Nothing shifts underneath them when a frontier provider reprices, rate limits, or deprecates a model. The stack stays where they put it.

Sovereignty

Data never leaves the perimeter

Prompts, completions, and cached context stay on their infrastructure or in your region. Routing decisions use model and latency metadata only, never prompt content.

Cost over time

Owned capacity keeps getting cheaper

At steady load, amortized hardware beats metered API pricing, and the advantage compounds every year the fleet runs. Your job is to price against that curve, and OpenInfer is how you get there.

White label

It ships as your platform, not ours.

Your brand, your console, your endpoint, your invoice. Your customers call your cloud and never see a third party in the path. OpenInfer runs underneath as the inference layer.

  • Per tenant API keys. Scoped per customer or project, with traffic isolated across the shared fleet.
  • Quotas and priority. Set limits per tenant and decide who gets capacity first when the fleet is busy.
  • Usage metering you can bill against. Per tenant token and capacity accounting, exportable into your billing system.
  • Your catalog. Publish the open models you want to sell, versioned across the fleet from one dashboard.
# your customer, your domain, your key
curl https://api.yourcloud.com/v1/chat/completions \
  -H "Authorization: Bearer $YOURCLOUD_API_KEY" \
  -d '{
    "model": "qwen3-32b-instruct",
    "messages": [{"role":"user","content":"hello"}]
  }'

# OpenAI compatible, so their existing code just works

Join the platform ecosystem

Bring OpenInfer to your fleet.

Tell us what silicon you run and which models your customers are asking for. We will get a node serving on your hardware and show you the sellable token math on your own numbers.

[email protected]

No hardware of your own yet? Try the hosted endpoint and see the platform running in production.