Live network · 270+ GPU providers · 40+ countries

Compute that moves as fast as your work

Native.Compute is a distributed GPU orchestration layer that routes AI and research workloads across institutional, cloud, and independent capacity — at up to 70% lower cost than centralized cloud.

Network snapshot

270+

Active GPU providers

40+

Countries

42M+

Verified tasks

70%

Lower cost vs. cloud

97.8%

Network reliability

Access modes

One network. Two ways in.

Native.Compute serves developers building on the network and institutions deploying at scale. Both access the same global infrastructure through the interface that fits how they work.

Developer access

Native.Compute Network

Full access to the distributed GPU network. Route inference, training, fine-tuning, and generative workloads across 270+ providers in 40+ countries. Task-based execution and real-time routing included.

  • Global GPU routing across heterogeneous providers
  • Task-based billing — pay for executed work only
  • Elastic scaling with instant access, no reservation queues
  • Verifiable execution records per task
  • Model hosting and custom model support
  • Real-time provider selection by cost and performance

Enterprise & institutional

Native.Compute Enterprise

The same global compute capacity accessed through standard cloud workflows. REST API endpoints for inference, training, and generative workloads. Fiat billing, unified usage dashboards, and drop-in integration with existing environments.

  • Standard REST API — works with existing tools and pipelines
  • Fiat billing with standard invoicing and reporting
  • Unified usage dashboard with analytics, logs, and allocation tracking
  • Dual routing — dedicated hosts for low-latency, global network for elastic capacity
  • Controlled environments for policy-constrained workloads
  • Cross-campus coordination and shared capacity models

Native.Compute Enterprise

Connects like cloud. Routes like a network.

Native.Compute Enterprise gives universities, research institutions, and enterprise teams direct access to global GPU capacity through the same interfaces they already use. Standard API calls. Standard billing. Standard dashboards.

Institutions submit workloads through a REST endpoint. Native.Compute routes each job to the best available capacity based on cost, performance, and deployment constraints — across institutional infrastructure, dedicated hosted environments, and the global provider network. Results return through the same unified pipeline.

For federated environments such as university systems with multiple campuses, Native.Compute coordinates across sites — routing preferentially to owned or institutional capacity, sharing underutilized resources across campuses, and accessing overflow capacity when local pools are at capacity.

Standard API, global reach

REST endpoints for chat, inference, images, and speech. Drop into existing workflows without rewrites or new tooling.

Unified usage and allocation dashboard

Compute usage, cost, allocation by project or team, and performance analytics in one interface. Supports structured research allocation models and reporting.

Policy-aware routing

Workloads route to approved environments based on data sensitivity, campus policy, or deployment constraints. Controlled architectures available for regulated research.

Federated campus coordination

Campus-owned resources serve local demand first. Idle capacity contributes to a shared pool. Overflow routes externally. Each campus retains its own policy and priority.

Enterprise workload flow

  1. 1

    Submit via standard REST API

  2. 2

    Policy and constraint evaluation

  3. 3

    Route to best available capacity

  4. 4

    Execute across institutional or global GPU

  5. 5

    Results returned via unified pipeline

  6. 6

    Usage logged to allocation dashboard

Standard invoice generated. No new tooling required.

How it works

Submit once. Execute anywhere.

Native.Compute connects institutional infrastructure, cloud capacity, and independent GPU providers into a single routable network.

01

Submit

Send a workload with your cost, performance, and data-locality constraints. Native.Compute handles the rest.

02

Route

Intelligent routing sends each job to the best available capacity — institutional-preferred, shared, overflow, or controlled environments.

03

Execute

Workloads run across heterogeneous GPU infrastructure. Researchers keep familiar workflows. Orchestration runs underneath.

04

Verify & Settle

Every task is verified and settled automatically. Verifiable execution records, task-based billing, and allocation reporting included.

Federated routing

Built for distributed environments.

University campuses, multi-site research institutions, and enterprise teams often hold compute across disconnected environments. Native.Compute coordinates those sources — routing to owned capacity first, then shared pools, then overflow — without forcing a single operating model across sites.

  • Campus workloads run on institutional capacity first
  • Idle resources from one site serve demand at another
  • Overflow routes to external capacity when local pools are at capacity
  • Sensitive workloads scope into controlled, policy-aware environments

Workload routing · live

1Institutional capacity · Primary
2Shared pool · Cross-site
3External overflow · Global network

Native.Compute

Verified · Billed · Reported

Use cases

Every workload type. One network.

Research

Model Training & Fine-Tuning

Elastic allocation for batch or streaming training. Scale up or down without reservation queues or wait times.

Research

Simulation & Experimentation

Run reinforcement learning, large-scale experiments, and domain-specific scientific workloads without capacity limits.

Institutional

Cross-Campus Compute Sharing

Idle campus GPU capacity serves demand across sites. Each campus retains its own policy and priority.

Enterprise

Production Inference

Serve LLMs and vision models at any scale. Predictable performance from pilot to global deployment.

Applied

Robotics, Edge & Simulation

High-throughput compute for autonomous systems, city infrastructure, marine, and space programs.

Developers

Generative Media & Agents

Video, image, audio and 3D generation for creative pipelines, agent workflows, and automation at scale.

Up to 70% lower than centralized cloud

Task-based billing means you pay for executed work. Idle costs, reservation queues, and region lock-in are eliminated.

Cloud

$$$

Reserved capacity & idle spend

Native

$

Pay per executed task

For institutions

Compute infrastructure without the infrastructure burden.

Universities and research institutions retain existing assets while NativelyAI operates them, transition older hardware into managed deployments, purchase new systems that Native.Compute deploys and runs, or access shared and overflow capacity on demand. One operating layer across all of it.

Asset OperationsResearch Compute GrantsCross-Campus SharingHardware DeploymentControlled ArchitecturesAllocation Reporting