#68 - Hyperbolic: The Ultimate Deep Dive
Transforming Decentralized AI Infrastructure
Stanford Blockchain Review
Volume 7, Article No. 8
✍🏻 Author: Hyperbolic Labs
⭐️ Technical Prerequisite: Intermediate
Introduction: The Foundation of Trust in Decentralized AI
Trust is the foundation of artificial intelligence. Without it, even the most powerful AI models are just black boxes generating unreliable outputs. At Hyperbolic, we are champions of decentralized AI infrastructure. This truth becomes even more critical when we adopt decentralization for AI—a move that promises greater accessibility and lower costs, but introduces new challenges in ensuring reliable results.
At Hyperbolic, we’re building a decentralized AI Cloud to democratize access to AI and push AI’s evolution forward. Today, we power 100,000+ developers with compute and inference. We are solving three pain points in AI today: Access to compute, Verifiability of AI Outputs, and Privacy of user data in decentralized AI systems.
The AI Accessibility Paradox
The acceleration of AI development has created an unfortunate paradox: while open-source AI models are theoretically available to everyone, actually using and implementing them remains out of reach for most. In today's rapidly evolving AI landscape, startups, developers, and researchers face significant challenges accessing the compute they need from traditional cloud providers to keep pace with their ideas.
Currently, sources of compute are centralized with companies such as AWS, Azure, and Google Cloud, creating an impression of scarcity of this valuable resource and allowing these providers to charge inflated prices. The cost of inference is becoming prohibitive as centralized inference platforms charge premium rates that can quickly drain development budgets, forcing teams to limit their experimentation or abandon promising projects entirely. The current pricing is heavily subsidized, but if inference is only controlled by centralized platforms, they will have the control to raise prices and trap developers.
Further, should a developer be able to afford compute from these sources, the inflexibility and complexity of implementing these solutions also serve as prohibitive barriers to innovation and scalability. Meanwhile, the growing gap between model release and availability has become an ever-widening chasm, difficult to cross.
A vast amount of GPU resources in data centers, mining farms, and individual machines worldwide remain underutilized while 2 billion computers around the world sit idle for over 19 hours a day. A common situation has also emerged where companies reserve machines from data centers for years, only to abandon strategies that leave their purchased resources unused. These scenarios all represent missed opportunities for monetization and efficient utilization.
Clearly GPU resources are not scarce, just uncoordinated. Without a way to connect those in need with those sitting on this golden resource, global GPU usage remains exceedingly inefficient and limits productivity in the field of AI.
These challenges make it clear that while a select few large organizations have the resources to leverage AI's full potential, the broader AI community remains constrained by accessibility barriers.
Hyperbolic's Integrated Solution Stack
Hyperbolic is creating an open and accessible AI ecosystem, where AI inference is available to all, democratizing this paradigm-shifting technology. Today, our solution stack consists of three core components that work seamlessly together:
Hyper-dOS: Our decentralized operating system that coordinates globally distributed GPU resources.
GPU Marketplace: Our platform connecting GPU suppliers with those who need compute resources.
Inference Service: Access the latest open-source models, at a fraction of the cost.
Agent Framework: Tools powered by Hyperbolic which allows for autonomous intelligence allowing agents to tap into our network, enabling them to evolve, self-replicate, and scale beyond limitations.
In the future, we will implement our groundbreaking Proof of Sampling (PoSP) protocol—the gold standard for verifying AI outputs. Developed in collaboration with researchers from UC Berkeley and Columbia University, PoSP addresses the trust problem in decentralized AI while preserving the cost advantages of decentralization.
We will also introduce a privacy layer for confidential computing, leveraging advanced cryptographic techniques to safeguard data and computations. This ensures strict confidentiality and privacy, allowing even Web2 enterprises with stringent data policies to securely utilize our network with the necessary security and privacy protections.
Let's dive deeper into each component to understand how Hyperbolic is systematically dismantling the barriers to AI accessibility.
Hyper-dOS: The Backbone of Decentralized Compute
The Hyperbolic Decentralized Operating System (Hyper-dOS) was developed to provide a robust and scalable backend architecture to efficiently manage a vast network of globally distributed GPUs. These compute resources are organized into a solar system-like network as independent planets coordinated by a sun cluster that governs and sustains each planetary cluster. This central cluster provides essential services and support to ensure the stability and efficiency of the overall system.
Hyper-dOS is so easy to use that, once installed on an underutilized machine, its compute power is seamlessly integrated with our distributed network in minutes for AI builders to quickly and easily rent scalable and cost-effective compute.
Hyperbolic’s GPU Marketplace: AI Compute at Global Scale
Compute is the limiting factor of AI. The more intelligence scales, the more power it demands. And yet, access remains locked in the hands of a few. AWS, Google, and OpenAI dictate availability, throttle supply, and extract value from the very builders driving innovation. It is a system built on scarcity, and it will not last.
Hyperbolic’s GPU Marketplace is changing that. We coordinate a globally distributed compute network that removes the artificial constraints imposed by cloud monopolies.
Permissionless access—anyone can contribute or utilize compute resources in under a minute.
Cost reductions of up to 75%—because AI should not be priced out of reach.
A network already running at scale—powering Stanford, NYU, Cornell, and the most advanced AI startups today.
This is not an experiment. It is not a testnet. Hyperbolic is live, in production, and expanding. If you are building AI, you will need compute, and if you need compute, you will end up here.
Hyperbolic’s Inference Service: Open-Source Intelligence at a Fraction of the Cost
AI is only as good as the models that power it. But those models remain locked behind restrictive APIs, inaccessible infrastructure, and corporate gatekeeping. Open-source AI should not be an afterthought—it should be the foundation.
Hyperbolic’s Inference Service gives developers frictionless access to cutting-edge models, without compromising privacy or control. With Hyperbolic you get access to:
The latest open-source models—served on a decentralized network, optimized for efficiency.
Absolute privacy—no prompt or output data is ever stored on our servers.
A cost model built for builders—because experimentation should not be a luxury.
We are already powering Andrej Karpathy, former director of AI at Tesla and OpenAI founding member. We support 38+ models, with 100,000+ developers building on our platform today.
AI is moving too fast to be waiting for permission. With Hyperbolic, you don’t have to.
Proof of Sampling (PoSP): The Gold Standard in Decentralized Verification
The Decentralized AI Verification Problem
Trusting the behavior of third party nodes poses a different challenge. How can one be certain that their inference output from a third-party node is valid?
Verification has been a challenge for decentralized technologies for a long time, with just a few methodologies emerging to address the issue. These traditional verification methods often rely on redundant computation or complex cryptographic proofs, which can introduce significant computational overhead to the system.
While such time and computation-heavy methods may have worked for the cryptographic use cases required in decentralized finance, they do not present as a practical solution for verification in decentralized AI inference. It isn't realistic to expect users of AI to wait for 10 days as the generated result of their request is verified. Further, current verification best practices can increase the cost of inference by up to 300%, negating any previously held cost advantage of decentralized GPU networks.
While decentralization promises to democratize access to AI, solutions often lack robust verification mechanisms, leaving developers uncertain about the reliability and consistency of their results. In the absence of trusted verification mechanisms, many remain hesitant to build on decentralized infrastructure—so does it really democratize access?
Trust in decentralized systems requires more than promises.
Hyperbolic's Game-Theoretical Approach to Verification
Hyperbolic's novel Proof of Sampling (PoSP) takes a fundamentally different approach to verification. Developed by our co-founder and CEO Jasper Zhang in collaboration with researchers from UC Berkeley and Columbia University, it secures the decentralized network by relying on a Nash Equilibrium-like approach to verifying outputs.
A Nash Equilibrium can be easily understood in the context of a train ticket verification system. Say a ticket costs $10, and to increase efficiency by reducing the number of train conductors that are required on each train, tickets are checked via a random sample of travelers with a $100 fine if a traveler is found to be without a ticket. One would think that this would open the door for grifters to feel more confident boarding the train without a ticket and thus getting a free ride. Logically, however, if the train conductors check just 1 in 10 tickets, there is a 10% chance that a grifter would need to pay a $100 fine, which works out to $10 for every ride in 10 rides, the same cost as the ticket. The optimized random sampling rate removes any incentive for a rider to act dishonestly while allowing for greatest possible efficiency for the train conductors.
Just as the train conductors don't have to check 100% of the tickets and are still able to ensure that 100% of the riders pay for their tickets, PoSP saves the computational time and energy of checking outputs from all nodes, and instead verifies a strategic proportion of outputs.
How PoSP Works in Practice
When implemented, the verification process begins when a client submits an inference request to the Hyperbolic network. The request is assigned to available third-party nodes based on computational requirements. Meanwhile, the system computes the appropriate verification sampling rate based on node reputation and stake, increasing for newer nodes in the network and decreasing for more nodes that have been proven reliable.
If an output is selected for verification, the system duplicates the work on trusted validator nodes. Should a validator find that the output is invalid, it will send the computation back through the orchestrator to trigger an arbitration process.
Because the cost of arbitration and the probability of validation are established to remain as a Nash Equilibrium, we have created a system where honest computation is the dominant strategy for participating GPUs. We have thus avoided having to spend the computational energy on checking every single result from our decentralized network, maintaining cost efficiency and ensuring practical usability.
PoSP ensures that every inference run on our network is verified without the significant computational overhead of other verification mechanisms, combining the benefits of decentralization with the reliability of traditional centralized systems. This represents a fundamental breakthrough in making the utilization of coordinated decentralized GPU resources both trustworthy and cost-effective.
Hyperbolic’s Agent Framework: The Future of Autonomous Intelligence
AI is evolving. It is moving beyond static models, beyond single-use applications. Autonomous intelligence is emerging—agents that act, adapt, and evolve without human intervention. But without infrastructure to support them, they remain locked in research papers and theoretical discussions.
Hyperbolic’s Agent Framework is designed for this future.
Self-evolving intelligence—AI that adapts in real time, without retraining.
Scalable AI agents—capable of operating autonomously across distributed networks.
A foundation for collective intelligence—where AI collaborates beyond human-defined constraints.
The Future of AI with Hyperbolic
As we continue to push the boundaries of what's possible in AI infrastructure, our commitment to empowering developers with the tools they need to build the future of AI remains consistent. The combination of our rapid model deployment, superior precision, comprehensive model selection, and groundbreaking cost efficiency creates an environment where innovation can flourish.
At Hyperbolic, our mission is to build an open and accessible AI ecosystem. With our integrated solution stack—Hyper-dOS, Proof of Sampling, GPU Marketplace, Inference Service, and Agent Framework—we are empowering AI builders looking to shape the future of AI by making resources available to all.