Web3/Crypto
AI/ML
DevTools/Cloud

Hyperbolic

AI infrastructure and open-access GPU cloud

CORE INFO

$50M
Total Funding
Series A
Round
2022
Founded
11-50
Team Size
San Francisco, CA
Headquarters

Hyperbolic provides GPU capacity, model-serving APIs, and managed infrastructure for AI startups, research institutions, enterprise AI teams, and GPU cloud providers. Customers use it to train and fine-tune models, run inference and production workloads, and manage GPU clusters across providers.

  • Series A1 funding totaled $30 million, led by Walden Catalyst and announced October 27, 2026; named participants included Dell Technologies Capital, General Catalyst, Vista Equity Partners, Cambium Capital, and Salience Capital.waldencatalyst.comaxios.com
  • Approximately 250,000 developers use Hyperbolic, according to figures reported in October 2026 coverage.cnbc.com
  • CNBC profiled Hyperbolic's proposed GPU exchange and physically settled AI-compute-futures concept as part of its broader marketplace strategy.cnbc.com

WHY WE WOULD WORK AT HYPERBOLIC

Expand AI Access

Help build the open-access AI cloud that makes powerful GPU infrastructure more affordable and available to developers, researchers, and startups worldwide.

Shape Core Infrastructure

Work on Forge, Hyperbolic's unified control plane for provisioning, virtualization, networking, orchestration, and utilization across heterogeneous GPU fleets.

Build What's Next

Help turn the Global Compute Grid into a new way to buy, sell, and resell GPU capacity while expanding Flux inference and Managed Kubernetes.

Small Team, Big Reach

Join an 11–50-person team whose work already supports more than 250,000 builders and customers including Modal, HeyGen, Pika, and Hugging Face.

Solve Hard Systems Problems

Tackle real challenges in GPU scheduling, multi-cloud orchestration, SGLang model serving, Kubernetes, networking, observability, and capacity markets.

Grow With Strong Momentum

Contribute during a major expansion backed by approximately $50 million in funding, including a $30 million Series A1 led by Walden Catalyst.

MARKET AND TRACTION

GROWTH TACTICS

  • Expands beyond GPU rentals through Forge, a unified control plane for provisioning, virtualization, networking, orchestration, workload management, and utilization improvement across GPU providers and neoclouds.

  • Builds recurring infrastructure demand through on-demand rentals, reserved clusters, private cloud deployments, Flux serverless inference, and beta Managed Kubernetes.

  • Developing a Global Compute Grid intended to enable buying, selling, and reselling GPU capacity, including potential spot instances, dynamic pricing, and capacity trading.
  • NOTABLE CUSTOMERS

  • Publicly referenced customers and users include Modal, Higgsfield, Daytona, HeyGen, Voio, RadixArk, Pika, Hugging Face, xAI, HappyRobot, Parasail, Reve AI, Gray Swan AI, Eigen AI, Neural Motion, Xent Labs, and UC San Diego.

  • Customer use cases include production inference, GPU sandboxing, model training, robotics-data generation, text-to-speech development, open-model evaluation, and academic research.
  • KEY METRICS

    ✦ KEY METRIC
  • Hyperbolic reports more than 250,000 builders or developers using its infrastructure, while third-party coverage cites approximately 250,000 developers.

  • The company has reported support for deployments from a single GPU to more than 1,000 GPUs, with clusters deployable in minutes.

  • Third-party coverage reported approximately $200 million in connected GPU capacity, approximately 30 neocloud suppliers, and a company target of $150 million in annualized revenue by year-end 2026; the revenue figure is a target, not realized revenue.

  • Hyperbolic has raised approximately $50 million in publicly announced funding, including a $30 million Series A1 led by Walden Catalyst.
  • COMPETITIVE ADVANTAGE

  • Hyperbolic combines GPU cloud access, serverless inference, provider-side orchestration, and a planned compute-liquidity network rather than operating solely as a GPU-rental marketplace.

  • Forge is designed to provide a consistent cloud experience across heterogeneous GPU fleets and multiple providers, while Flux offers an OpenAI-compatible API with streaming, token-based billing, model routing, and usage monitoring.

  • The platform emphasizes open access, automation, and rapid provisioning; independent reviews also note that third-party supply can create variability in networking, hardware configuration, reliability, and software environments.
  • PRODUCT AND TECH

    GPU Cloud Infrastructure

    On-demand, reserved, and private NVIDIA GPU capacity supports training, fine-tuning, inference, research, batch processing, and production workloads. Customers can deploy from single GPUs to large clusters.

    Forge Control Plane

    Forge provides GPU providers and neoclouds with provisioning, virtualization, networking, orchestration, workload management, and utilization-improvement software across heterogeneous GPU fleets.

    Flux Inference API

    Flux is an OpenAI-compatible, serverless inference API with streaming responses, token-based billing, model routing, and GPU-backed model serving. It is built on Hyperbolic's Forge infrastructure.

    Managed Kubernetes

    Hyperbolic's beta Managed Kubernetes service creates GPU-backed clusters while managing the Kubernetes control plane and worker-node provisioning. It supports NVIDIA GPU Operator and NVIDIA Network Operator add-ons.

    Global Compute Grid

    The planned Global Compute Grid is designed to let participants buy, sell, and resell GPU capacity. It extends Hyperbolic's infrastructure platform toward more liquid and flexible compute markets.

    COMPANY CULTURE

    Values

  • Innovation and pushing the boundaries of AI infrastructure

  • Open access to affordable, scalable compute

  • Collaboration with developers, researchers, and infrastructure providers

  • Automation that reduces friction for builders
  • Operating Principles

  • Developer-focused and infrastructure-first

  • Reduce barriers to AI development

  • Make GPU capacity more accessible and efficient

  • Build open, scalable foundations for AI
  • Work Style

  • Fast-moving and execution-oriented

  • Technical, research-friendly environment

  • Small-team ownership across complex infrastructure problems

  • Collaborative approach across cloud, AI, and hardware ecosystems
  • Learning & Growth

  • Exposure to GPU orchestration, Kubernetes, inference, and distributed systems

  • Opportunities to work across cloud computing and emerging compute markets

  • Research-informed culture shaped by quantitative and AI expertise

  • Continuous experimentation with new infrastructure models