theOpen-AccessAI Cloud

Hyperbolic gives 200,000+ builders flexible access to GPU compute — from self-serve On-Demand GPUs to Reserved capacity and long-term Private Cloud infrastructure.

Hyperbolic makes building and running AI hyper simple

Deploy Affordable GPU Clusters, On Demand

Provision H100, H200, and B200 capacity in minutes without quota games, long procurement cycles, or unnecessary sales calls. Hyperbolic gives teams fast access to GPU clusters through a global network of compute providers. Use On-Demand when you need flexible capacity for experimentation, training, fine-tuning, inference, or short-term production workloads.

Creating Your Instance

Buckle up! 💨 We're deploying your GPUs...

Self-serve GPU compute, built for flexibility

Launch and manage on-demand GPU instances in minutes from a clean, intuitive dashboard. No sales calls, no forms, and no long wait times. Scale usage up or down as needed and only pay for the compute you use.

We provide affordable GPU access for those at the edges of AI

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    AI Natives

    Scale AI workloads without waiting on traditional cloud capacity.

    Hyperbolic gives startups and AI-native teams fast access to high-performance GPU capacity for training, fine-tuning, inference, and production workloads. Start on demand, scale into reserved clusters, or move to dedicated Private Cloud infrastructure when your workload needs predictable capacity.

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    Researchers

    Get the compute you need to move from idea to experiment faster.

    Hyperbolic helps researchers access H100, H200, B200, and other high-performance GPUs without long waitlists, rigid contracts, or complex procurement cycles. Spin up capacity when experiments are ready, scale as workloads grow, and keep your team focused on research instead of infrastructure.

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    Compute Providers

    Turn available GPU capacity into revenue through Hyperbolic's global AI cloud network.

    Hyperbolic helps data centers and infrastructure providers connect their GPU supply to real AI demand. Through Forge and Hyperbolic's managed compute platform, providers can support on-demand, reserved, and private cloud deployments while reaching customers building at the frontier of AI.

High-Performance Infrastructure.

Deploy GPUsDeploy GPUs

200K+ Engineers

leveraging Hyperbolic’s AI infrastructure

Minutes, Not Weeks

to deploy a cluster

Zero Quota Limit

for GPU rentals

No Long-Term Lock-In

scale up, scale down, or reserve capacity when you’re ready

Hear from the humans

using Hyperbolic

Clém Delangue

Clém Delangue

CEO & Co-Founder of Hugging Face

Hyperbolic’s speed in delivering the latest open-source models and strong commitment to the AI developer community is amazing. With their API live on Hugging Face, developers worldwide can build faster than ever.

What teams build on Hyperbolic.

From frontier training to long-running production workloads, Hyperbolic gives AI teams flexible access to high-performance GPU compute through On-Demand GPUs, Reserved Clusters, and Private Cloud.

Foundation-model training

Reserved multi-node H100, H200, and B200 clusters with high-performance interconnects for long, uninterrupted training runs.

Fine-tuning & LoRA

Bare-metal single-node and small multi-node capacity for fast iteration on open-source checkpoints and custom models.

Batch compute jobs

On-demand GPU capacity for experiments, evaluations, data processing, and other compute-heavy AI workloads.

Long-running training jobs

Run extended training workloads on dedicated GPU capacity with predictable availability, stable performance, and fewer interruptions.

Agent automation

Programmatically launch and manage GPU resources for agent workflows, evaluation pipelines, and other compute-heavy automation.

Private infrastructure

Private Cloud environments with dedicated hardware, isolated networking, and deployment support for teams ready to scale long term.

The basics, answered.

Hyperbolic is the Open-Access AI Cloud for teams that need fast, flexible access to GPU compute. The platform gives startups, researchers, AI labs, and enterprises one place to launch on-demand GPUs, reserve dedicated capacity, or deploy private GPU infrastructure for training, fine-tuning, inference, and production AI workloads.

Instead of waiting on legacy cloud quotas, long procurement cycles, or rigid contracts, teams can start with self-serve GPU instances and scale into reserved or private infrastructure as their workload requirements grow.

You can launch an on-demand GPU instance in minutes. Create an account, add a payment method, choose your GPU configuration, and connect over SSH.

Hyperbolic supports workloads ranging from single-GPU experimentation to larger multi-GPU and multi-node deployments. For teams that need guaranteed access to specific GPUs such as H100s, H200s, or B200s, Reserved and Private Cloud options are available.

No. On-demand GPU instances are pay-as-you-go, so you only pay for the compute you run. There are no long-term commitments required to get started.

For teams running persistent training, fine-tuning, inference, or production workloads, Reserved capacity provides dedicated GPU access at discounted rates. This gives you more predictable availability and pricing without forcing you into the multi-year commitments often required by legacy cloud providers.

Hyperbolic pricing depends on the compute model you choose.

On-demand GPUs are usage-based and billed by the hour, making them a good fit for experimentation, burst workloads, short training runs, evaluation jobs, and flexible inference needs.

Reserved GPUs are priced at discounted prepaid rates for teams that need dedicated, always-on capacity. This is usually the better fit when your workload is predictable, production-critical, or capacity-sensitive.

Private Cloud pricing is customized based on GPU type, cluster size, networking, isolation, compliance, and operational requirements.

Hyperbolic offers three ways to access GPU capacity.

On-Demand is best when you need GPUs right away with no commitment. It is the most flexible option and is billed based on usage.

Reserved is best for steady production workloads that need dedicated capacity at a discounted rate. It requires a shorter-term commitment, usually under one year.

Private Cloud is best for teams scaling long term. It provides dedicated infrastructure, more control, and the lowest pricing through longer-term agreements.

Yes. Hyperbolic is built for secure GPU workloads, with isolation controls that vary by infrastructure tier.

On-Demand GPU instances run on secure multi-tenant infrastructure designed for fast, flexible access. Workloads are isolated from other users, with network isolation and instance cleanup between sessions.

Reserved capacity gives your team dedicated GPU resources for more predictable performance and stronger workload control. Depending on the configuration, this can include bare-metal access, direct SSH, and dedicated infrastructure for your workloads.

Private Cloud provides the strongest isolation model for teams with stricter security, compliance, or enterprise requirements. It can include single-tenant infrastructure, isolated networking, SSO, audit logging, and compliance support.

For sensitive workloads involving proprietary models, regulated data, or strict internal security requirements, Reserved or Private Cloud is usually the better fit.