Product

GPU Network

Capabilities

Scale workloads, not servers.

Builders are choosing Dispersed to access compute that is fast, reliable, and economically scaled.

Quickstart

Execute workloads without hardware overhead.

No servers to spin up. No clusters to maintain. No infrastructure to manage. Submit a container, the network executes it, and the resources tear themselves down the second the job is done.

Maximum Cost Efficiency

Pay only for execution time.

A globally distributed fabric of GPUs ready to serve jobs anywhere. Get scalable access at an affordable price. Efficiently and transparently.

Container-Native

Package once, run anywhere.

Standard OCI containers. Identical environments locally and globally. If it runs on your laptop, it runs on the network.

Elastic Scaling

One request to millions.

Scale from a single workload to globally distributed parallel execution instantly.

Global Execution

Run anywhere compute exists.

Deploy workloads across decentralized GPU nodes without regional infrastructure dependencies.

Stateless Infrastructure

Build, ship, sleep.

Execute accelerated workloads without provisioning servers, maintaining instances, or managing hardware lifecycle.

GPU Network v1.0

Features

Ready-to-use GPUs. Perfect for demanding, modern AI tasks.

Ready to scale your AI without breaking the bank? Onboard in minutes and deploy your first workload today.

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Generative AI

Generate images, video, audio, and multimodal outputs generated with massively parallel GPU execution.

Model Training

Train and fine-tune AI models across distributed GPU infrastructure without cluster oversight or overhead.

Edge Computing

Execute low-latency workloads closer to users without maintaining globally distributed hardware infrastructure.

Data Creation

Accelerate preprocessing, transformation, feature extraction, and batch compute pipelines at global scale.

Agentic Frameworks

Powering the next generation of autonomous AI platforms — high concurrency, low latency, no cold-start tax.

GPU Network v1.0

Workloads

If your workload requires a GPU and runs in a container, it runs on Dispersed.

AI & ML

AI & Machine Learning

Deploy LLM inference pipelines, text summarization and classification, embedding generation, multimodal processing, model evaluation, and agentic automation.

Media

Media & Content Processing

Accelerate image and video generation, transcoding and enhancement, procedural media, synthetic data creation, and massive batch asset processing.

Simulation

Simulation & Spatial Compute

Power physics and environment simulations, 3D and spatial data processing, procedural generation, scientific modeling, and digital twin systems.

Data

Data & Pipeline Compute

Execute large-scale data preprocessing, complex transformation pipelines, feature extraction, parallel batch jobs, and scheduled compute tasks.

GPU Network

Security

Trustless Architecture.

An API that provides control plane security.

Dispersed secures the control plane while treating compute nodes as untrusted, ensuring your job management is protected without requiring trust in individual node operators.

Cryptographic API Signing

Every API request is cryptographically signed to prevent tampering, spoofing, and replay attacks.

Client-Side Secrets

Credentials remain client-side and are never permanently stored on distributed compute nodes.

Isolated Workloads

Workloads execute in isolated container environments with a controlled lifecycle and automatic teardown. There is no persistent access.

Auto-Provisioned

GPU nodes provision on demand and terminate automatically after workload execution completes.

GPU Network

How it works

A composable protocol for compute. No vendor lock-in. No black box APIs.

From container to execution in no time.

Step One

Containerize

Package your models, code, and dependencies into a standard container — once.

Step Two

Trigger

Launch via API, schedulers, or automated pipeline events.

Step Three

Execute

Nodes instantly run inference, processing, or simulation tasks at scale.

Step Four

Output

Export results to your storage, APIs, databases, or downstream systems.

Step Five

Auto-Shutdown

Resources terminate the moment jobs complete. No persistent infrastructure.

~ / dispersed · run.sh

On-Demand Compute for AI Jobs.

Scale workloads, not servers.

Ready-to-use GPUs. Perfect for demanding, modern AI tasks.

If your workload requires a GPU and runs in a container, it runs on Dispersed.

Trustless Architecture.

A composable protocol for compute. No vendor lock-in. No black box APIs.

Ready to scale your AI without breaking the bank?