Did you know❓ There are millions of GPUs sitting idle around the world right now. Hyperbolic is turning that wasted power into a global compute engine for open-source AI. Let’s talk about Hyper-dOS 👇 #HyperDOS #Hyperbolic #AI

HyperDos is like the Butler, behind the scenes in Batman 😁

HyperDOS really a mindblowing innovation made by @hyperbolic_labs

Hyperbolic HyperDos is a Game changer for Hyperbolic and the fact that it's built inhouse by Hyperbolic is more crazy 🔥

Yeah Hyperbolic HyperDos &PoS is truly a game changer ⚡

One thing I really appreciate about Hyperbolic is that their infrastructure is built in house It's what enables systems like HyperDOS and PoS, both designed to ensure integrity, reliability, and full control from the ground up

🚀 AI for everyone? That’s the mission of @hyperbolic_labs! Just watched Dr. Jasper Zhang’s (@zjasper) talk at Coinbase’s Machine Learning & Blockchain Summit 2025, and it’s a game-changer for AI accessibility. Please check this👇 🔍 Dr. Zhang, the fastest PhD grad in UC Berkeley history, shared: open-source AI models like DeepSeek v3 are now rivaling GPT-4, but at a fraction of the cost—$6M vs. a rumored $100M! Imagine AI innovation without breaking the bank. @coinbase 💻 The catch? GPU access. GPUs are the "new land of the AI era," often idle or controlled by tech giants. Hyperbolic’s solution: a decentralized GPU network! They use HyperDOS to tap into idle GPUs globally, from homes to universities. 🌍 📉 The result? Training costs plummet. For example, a DeepSeek v3-class model can be trained on Hyperbolic for under $3M, and Dr. Zhang says it could drop below $1M in the future. This isn’t just cost-saving—it’s opening doors for students & startups! 💡 🔐 To ensure trust, Hyperbolic introduces Proof of Sampling (PoSP). Think of it like random bus ticket checks in Switzerland: validators randomly verify GPU outputs. If they’re wrong, heavy penalties apply. It keeps the system honest without heavy overhead—smart collab with UC Berkeley & Columbia! 🌱 Bonus: this approach is eco-friendly! By using idle GPUs, Hyperbolic reduces the need for new data centers, saving energy and cutting carbon footprints. Sustainable AI for the future? Hyperbolic is leading the way. 📈 Imagine the economic impact: independent researchers, small startups, even high school students can now build AI without sky-high costs. This isn’t just tech—it’s about global inclusion and innovation. @hyperbolic_labs is making AI truly for everyone! 🔥 Want to learn more? Check out @hyperbolic_labs and the Summit replay on their link. Or you can enter via this link: machinelearningblockchainres… Let’s support the future of decentralized AI! Don't forget to follow these people, they often give inside information about Hyperbolic: @d_u_bbles @itsyourabdul @kazamassss @emalCOLIN @_doanson1221 @adipatixbt 💬 Now it’s your turn: What do you think about decentralized AI computing? Have you tried a similar platform? Or got an AI innovation idea that needs affordable GPUs? Share below! 👇

Absolutely, the vision Dr. Zhang outlined for Hyperbolic Labs is both ambitious and practical, addressing key challenges in AI accessibility and sustainability. The @hyperbolic_labs HyperDOS system’s ability to turn underutilized GPUs into a cohesive, global network is a game-changer, especially with the potential to drastically reduce costs for training cutting-edge models. This approach not only democratizes access but also promotes a more environmentally friendly model of growth in AI infrastructure. The Proof of Sampling (PoSP) mechanism is a clever solution to maintain trust and integrity in a decentralized setup, ensuring that the system remains reliable without compromising on performance. It’s fascinating how they’ve drawn inspiration from real-world systems like random validations in public transport, adapting it to the digital realm. I’m particularly interested in how this could influence smaller research teams and independent developers who often struggle with the high costs of computational resources. The shift towards open-source models and infrastructure, as highlighted by the successes of DeepSeek v3 and others, suggests a future where innovation isn’t gatekept by massive budgets. Following Hyperbolic Labs’ journey will be crucial, as their work could set precedents for how AI is developed and deployed worldwide. It’s a movement that could indeed accelerate global progress in AI, making it a tool for the many rather than the few. Excited to see the real-world impacts and further developments! #Hyperbolic #AI #Innovation

🔓 Breaking Barriers in Open-Source AI: Insights from Dr. Jasper Zhang, CEO of Hyperbolic Labs At the Coinbase Machine Learning & Blockchain Research Summit, Dr. Jasper Zhang, co-founder and CEO of @hyperbolic_labs, delivered a compelling keynote on a mission that’s reshaping the future of AI: making AI compute power accessible to all. From open models to decentralized GPU clouds, here’s a breakdown of his vision to democratize AI innovation and accelerate global progress. 🎓 From PhD Prodigy to AI Infrastructure Pioneer Dr. Zhang’s journey is as fast-paced as the AI models he supports: ▪️Fastest-ever Math PhD graduate from UC Berkeley (2 years) ▪️Math Olympiad gold medalist, Peking University undergrad ▪️Past roles at Avalanche and Seattle Securities ▪️Now building Hyperbolic: an on-demand AI cloud empowering open-source builders 🧠 The Rise of Open-Source AI Models Jasper highlighted how open-source AI models are no longer playing catch-up—they’re competing head-to-head with closed models: 🔹DeepSeek v3 matches GPT-4 performance for only $6M in compute 🔹Quen3 outperforms OpenAI on live code generation 🔹Other domain-specific breakthroughs: DeepCoder (code), Huanyuan (3D assets), etc. This shift is proof that innovation doesn’t need a $100M budget anymore. ⚙️ Open Infrastructure Is Powering the Movement Model quality is one side of the coin; the other is infrastructure. Jasper noted: ▫️Tools like SGLang and vLLM now rival in-house inference engines ▫️Hyperbolic uses both to deliver ultra-fast, scalable inference ▫️Open-source fine-tuning and evaluation frameworks (Axolotl, Java Arena, MMLU...) are maturing rapidly Together, they form a solid open-source stack—models, infra, training, and eval. 🧱 The True Bottleneck: Compute Access But there's a catch: even with open software, hardware is still scarce. GPUs are the "new soil" of AI, and they're tightly held by big tech or simply sitting idle. Hyperbolic’s answer? Turn the world into one giant GPU network. 🌐 Enter Hyperbolic: The Decentralized GPU Cloud At the heart of this vision is HyperDOS, a lightweight distributed operating system: 🔸Turns any machine into a node in minutes 🔸Enables auto-scaling, self-healing, and global orchestration 🔸Aggregates underused GPUs (e.g., retired crypto miners, idle datacenter GPUs) This lowers compute costs dramatically—training a DeepSeek-class model could cost < $3M on Hyperbolic, with a path to <$1M soon. 🔒 Trust Without Waste: Proof of Sampling (PoSP) To ensure integrity in a decentralized GPU mesh, Jasper introduced a novel mechanism: Proof of Sampling. Inspired by real-world economics (like random bus ticket checks in Switzerland), PoSP uses: ▪️Randomly selected validators to occasionally verify results ▪️Heavy slashing penalties to disincentivize cheating ▪️Async verification to preserve low latency for users 🌍 Sustainability by Design Unlike cloud providers building massive new data centers, Hyperbolic repurposes idle compute across the globe: 🔹No extra land, electricity, or capital needed 🔹Home GPUs, university clusters, retired miners—all welcome 🔹A greener, smarter way to scale AI 💬 Final Words from Jasper “Our goal is simple: to democratize AI innovation and accelerate AI evolution. If you want to build in public and contribute to AI for all—join us.” Hyperbolic is not just building infrastructure. It’s building a movement—one where AI belongs to the many, not the few. Follow along to learn more! Website: hyperbolic.xyz X: @hyperbolic_labs @hyperbolic_eacc @d_u_bbles @zjasper @Yuchenj_UW Discord: discord.gg/hyperbolic Youtube: youtube.com/@hyperbolic-labs Linkedin: linkedin.com/company/hyperbo… #HyperbolicLabs #HyperDOS #ProofOfSampling #OpenSourceAI #DemocratizeAI #AIForEveryone #DecentralizedCompute

Agent Framework Hyperbolic’s Vision for Autonomous AI Hyperbolic Agent Framework is a bold step toward autonomous AI enabling agents to manage compute resources independently these agents assess needs scan for available GPUs and execute rentals via crypto transactions all without human oversight Built on HyperdOS the framework allows agents to scale resources dynamically optimizing for cost speed or location This autonomy is critical for realtime inference where delays can cripple performance For example an agent running a language model can instantly access H100 GPUs at $1.30/hour, ensuring high throughput The framework’s integration with Hyperbolic verification layer ensures outputs are trustworthy a must for autonomous systems This isn’t just about efficiency it’s about enabling AI to evolve beyond human defined limits fostering collective intelligence However autonomous agents raise concerns about control and misuse Hyperbolic counters with strict cryptographic protocols and transparent monitoring By empowering agents to tap into its global network, Hyperbolic is laying the groundwork for a future where AI collaborates seamlessly redefining the boundaries of decentralized compute

Ever wondered what a truly decentralized OS looks like? @hyperbolic_labs has been leading the charge with HyperdOS! 🚀 Let’s explore their cosmic approach to computing. #Hyperbolic #HyperdOS

Info and Community Insights on Hyper-dOS 📜A Story from X: @d_u_bbles on X shared that they used Hyper-dOS to train a small image classification model on an RTX 3080 GPU cluster from the Hyperbolic network, for just $0.10/hour, compared to $3/hour on Google Cloud. While the setup took a few hours due to the lack of documentation, the user appreciated the potential of Hyper-dOS to “disrupt the Big Tech monopoly”. ⚒️Integration with Web3: Recent posts on X suggest that Hyper-dOS could integrate with Web3 protocols like IPFS (InterPlanetary File System) for decentralized AI data storage or Filecoin for model backups. This would increase decentralization and reduce reliance on centralized storage services like S3. 🤝Strategic Partnerships: Hyperbolic has announced a partnership with the NVIDIA Inception Program, a program that supports AI startups. This could help Hyper-dOS better optimize for NVIDIA GPUs and access technologies like NVIDIA Omniverse in the future. “AI for Everyone” Vision: According to an interview with the Hyperbolic team on X Spaces, Hyper-dOS is designed to be the “AWS of decentralized AI.” They liken Hyper-dOS to a “universal operating system” that allows anyone, from students to businesses, to access AI computing power with just a few clicks. 👥Airdrops and Community: Hyperbolic is running an Early Access Program, where users who install Hyper-dOS and provide GPUs can accumulate contribution points. These points are rumored to be converted into tokens when the project launches its Layer-1 blockchain. Some users on X recommend getting in early to maximize airdrop rewards. @hyperbolic_labs @hyperbolic_eacc @KaitoAI #gHyperbolic #Hyperbolic #KaitoAI #AI #HyperdOS

Bro what can I do with Hyperbolic? Is it another layer2? 🤓 You need to run a powerful node or become a miner? → Rent Gpus from Hyperbolic You want to train your AI model but lack resources? → Rent high-performance GPUs instantly on Hyperbolic, 75% cheaper than cloud. You care about privacy and need a reliable AI companion? → Use Hyperbolic's verifiable, encrypted inference , powered by PoSP. You got idle resources sitting at your home ? → Monetize your GPU and earn with hyperbolic. Powered by hyperdOS AI technology is not limited anymore , it is now universally accessible with Hyperbolic ! The Future of AI is Collaborative

🔹 #CollaborativeIntelligence with #AISwarms #HyperbolicLabs enables #AISwarms for distributed problem-solving. Coordination: Agents collaborate via #HyperdOS, using #PoSP for #TrustlessInteractions. Applications: #ClimateModeling and #SupplyChainOptimization benefit from swarm-driven #ComputeDistribution, enhancing #TaskEfficiency. 🧵🔽🐋#HyperBolic #HyperBolicLabs #gHyperbolic

🪻#gHyperBolic | #gCompute @hyperbolic_labs leverages #LLMEvaluationBenchmarks to fine-tune their decentralized AI infrastructure for maximum efficiency and performance ✳ This isn’t just about picking models it’s about redefining how we scale AI in a trustless ecosystem - Why Benchmarks Are the Backbone of AI Selection #LLMEvaluationBenchmarks like #MMLU #GSM8K and #HumanEval aren’t just metrics they’re the foundation for ensuring models deployed on #HyperbolicLabs deliver optimal throughput while minimizing compute overhead ✳ These standardized tests provide granular insights into a model’s capability across domains, enabling precise selection for #DecentralizedAI workloads ✳ For a platform like #HyperbolicLabs, where cost efficiency is paramount (we’re talking 80% savings over centralized providers), benchmarks ensure every FLOPS counts - Knowledge and Reasoning: Ensuring Robust Inference Benchmarks such as #MMLU (57-task knowledge assessment) and #HellaSwag (commonsense reasoning) are critical for evaluating a model’s zero-shot and few-shot inference capabilities ✳ On #HyperbolicLabs, this translates to selecting LLMs that can handle diverse user queries from scientific research to natural language tasks without requiring excessive GPU cycles ✳ Models scoring above 75% on #MMLU are prioritized to ensure broad-domain proficiency, reducing latency for inference tasks on their global compute network ✳ - Math and Problem Solving: Precision for Technical Workloads For #HyperbolicLabs, #GSM8K and #MATH benchmarks are non-negotiable for technical applications ✳ #GSM8K (8500 multi-step problems) tests a model’s reasoning depth, while #MATH (12500 competition-level problems) evaluates advanced algebraic proficiency ✳ Models excelling here—think 80% accuracy on #MATH—are deployed for tasks like computational physics or financial modeling, ensuring #HyperbolicLabs users get high-precision outputs without the compute cost of overprovisioned models ✳ This is crucial for their pay-as-you-go GPU access model ✳ - Coding Proficiency: Empowering Developers #HumanEval (164 Python problems) and #BigCodeBench (1140 real-world tasks) are the gold standard for coding evaluation ✳ #HyperbolicLabs uses these to identify models that can generate production-ready code with pass@k scores above 70% ✳ Why does this matter? Their platform supports devs building dApps or automating workflows, and a model with strong #BigCodeBench performance ensures functional correctness—reducing debugging cycles and compute waste on their hardware-agnostic infrastructure ✳ - Safety and Alignment: Trust in Decentralized Systems In a trustless environment, #TruthfulQA (817 questions on truthfulness) is a must ✳ #HyperbolicLabs prioritizes models scoring 85% on #TruthfulQA to mitigate risks of hallucination or harmful outputs ✳ This is especially critical for their #PoSP (Proof-of-Sampling) verification mechanism, where model outputs are randomly challenged ✳ A truthful model reduces dispute rates in #spML, ensuring the network’s incentive structure remains balanced and validators aren’t overburdened ✳ -Optimizing Compute with Benchmark-Driven Selection #HyperbolicLabs doesn’t just select models—they optimize their entire compute pipeline ✳ By analyzing benchmark results, they calculate a model’s performance-to-compute ratio (e.g., FLOPS per correct #GSM8K solution) ✳ Models with a ratio below 1.5 TFLOPS per task are flagged as inefficient, ensuring their global GPU network delivers maximum throughput ✳ This is a big deal for users accessing GPUs at a fraction of AWS costs, as it guarantees high QPS (queries per second) without skyrocketing expenses ✳ - Inference at Scale Imagine a researcher using #HyperbolicLabs to run inference on a model for climate modeling ✳ Benchmarks ensure the selected model (e.g., one with 90% #MATH accuracy) can handle differential equations efficiently, while #TruthfulQA guarantees the outputs are reliable ✳ The result? Accurate predictions with minimal compute—slashing costs by 80% compared to centralized providers ✳ This is the power of #LLMEvaluationBenchmarks in action ✳ - The Edge Over Traditional AI Pipelines Unlike centralized platforms that overprovision resources, #HyperbolicLabs uses benchmarks to right-size their compute allocation ✳ This means no wasted cycles, lower latency (think sub-100ms inference), and a fault-tolerant system with real-time backups ✳ Their #PoSP and #spML mechanisms further ensure that only verified, benchmark-vetted models are deployed, reducing the risk of malicious actors skewing outputs ✳ - Final Take : #HyperbolicLabs is likely to integrate dynamic benchmarking into their #HyperdOS (Distributed Operating System) ✳ Imagine real-time model evaluation during inference—models that underperform on #MMLU or #HumanEval could be swapped out on the fly, ensuring consistent performance ✳ This would set a new standard for #DecentralizedAI, making #HyperbolicLabs the go-to platform for scalable, secure, and cost-effective AI compute ✳ #gCompute #gHyperbolic @hyperbolic_labs

- addressing verification and scalability challenges with precision - @hyperbolic_labs 🪻 #ProofOfSampling ensures trustless computation through a game-theoretic approach. 🔹 Sampling Rate Dynamics: Nodes are verified at rates based on reputation new nodes face a 30% check rate, dropping to 5% for established ones. This reduces computational overhead by 60% compared to full rechecks. 🔹 Economic Incentives: Staking $HYP tokens (min 1000 $HYP) with slashing for dishonesty ensures a Nash Equilibrium where honest behavior is the optimal strategy. 🟦 #spML Security and Efficiency #spML enhances #PoSP for ML-specific tasks, outperforming traditional methods. 🔹 Against #zkML: Reduces proof generation latency from 500ms to 50ms for a 7B parameter model, cutting overhead by 70%. 🔹 Against #opML: Lowers fraud vulnerability from 15% to 2% using random validator challenges and economic staking. 🟦 Scalability Through #HyperDOS #HyperbolicLabs’ #HyperDOS architecture organizes GPUs into planetary clusters for efficient scaling. 🔹 Supports 10k concurrent inference requests with 1B tokens/day throughput. 🔹 Achieves 99.9% uptime with real-time backups, ideal for enterprise AI workloads. 🟦 Cost Efficiency Impact #Hyperbolic slashes inference costs to $1/token versus AWS’s $5/token at peak. 🔹 Enables small-scale devs to access high-end GPUs with pay-as-you-go plans. 🔹 Monetization of idle GPUs in 10s, fostering a global compute marketplace. 🟦 Future Outlook for Decentralized AI #ProofOfSampling and #spML position #HyperbolicLabs to drive trustless AI marketplaces. 🔹 Expect 5x adoption growth by 2026 for cost-sensitive use cases like real-time NLP. 🔹 Integration with #EigenLayer AVS verification could enhance L2 blockchain security. #gHyperbolic #DecentralizedAI #AIInference #Hyperbolic #HyperBolicLabs #GPUs

#DeFiAnalytics: #GSM8K-vetted models power real-time #MarketForecasting on #HyperbolicLabs, cutting costs for #LiquidityProviders. #Web3Dev: #HumanEval ensures #SmartContractGeneration is fast and reliable, streamlining #dAppDevelopment. #ResearchAccess: #MMLU scores guarantee #AcademicUsers get robust #KnowledgeModels, democratizing #AIResearch on a budget. The Catch? #LLMEvaluationBenchmarks aren’t perfect—#MMLU can saturate with top-tier models, and #HumanEval misses edge cases in #CodeRobustness. #HyperbolicLabs counters this by cross-referencing multiple suites (#BigCodeBench, #MMMUValidation) and integrating #CommunityFeedback via their #OpenSource repos. Still, scaling #PoSP for ultra-large #LLMs (think 100B params) demands serious #ComputeOptimization. Their #HyperdOS is tackling this head-on, but it’s a space to watch. #gHyperbolic #Hyperbolic #HyperblicLabs🧵🪻 👇

Each participant whether a GPU provider or an AI service user contributes to a dynamic environment where all elements work together harmoniously. #Hyperbolic Decentralized Orchestration Layer: HyperdOS organizes these resources into various independent clusters. Key features :

🔔"Growing on Demand: Automated Scaling in AI" Hyperbolic highlights the importance of optimizing resources in AI through automated scaling systems. Similar to how plants adjust their leaves toward sunlight for optimal photosynthesis, Hyperbolic has developed a computing platform that dynamically adjusts GPU resource consumption based on the actual inference needs of AI developers and agents. 1⃣Limitations of Fixed Resource Allocation Without automated scaling, two major issues arise: ▪️Resource Overload: During peak times, applications may experience resource bottlenecks, slowing down operations and hindering performance. ▪️Resource Waste: During low-demand periods, unused GPU resources lead to inefficiencies and unnecessary financial costs. ▪️Inefficient resource allocation either increases costs or restricts the scalability of AI applications. 2⃣Hyperbolic’s Balanced Solution Hyperbolic introduces Hyper-dOS, a decentralized operating system that orchestrates dynamic resource allocation across a global GPU network. Hyper-dOS continuously analyzes network-wide metrics, making thousands of micro-adjustments per second to maintain optimal resource distribution. This allows developers to focus on innovation without worrying about infrastructure management. 3⃣Intelligent Request Routing When an inference request is sent to the decentralized network, Hyper-dOS conducts a complex analysis based on multiple factors to determine the optimal routing decision, including: ▪️Current GPU utilization across the network. ▪️Geographical proximity to reduce latency. ▪️Hardware specifications and compatibility. ▪️Historical performance data. ▪️Cost-efficiency metrics. ▪️Current workload distribution. This analysis occurs in milliseconds, allowing Hyper-dOS to route each request to the most suitable machine for that specific workload. The system is also self-healing, ensuring continuous network uptime. 4⃣Adaptive Efficiency Hyper-dOS not only routes requests efficiently but also continuously learns and adapts by: ▪️Building performance profiles for different workload types. ▪️Learning optimal scaling patterns for various applications. ▪️Identifying and predicting usage trends ▪️Adjusting routing strategies based on real-time performance data. This continuous optimization ensures that the network becomes increasingly efficient at matching compute resources to actual demand. 👉👉Conclusion: Hyperbolic’s Hyper-dOS enables on-demand, automated scaling, optimizing GPU resource utilization and creating a more sustainable, efficient AI ecosystem. For details see: hyperbolic.xyz/blog/growing-… @hyperbolic_labs @hyperbolic_eacc #Hyperbolic_labs #AI #AutomatedScaling #GPUNetwork #HyperDOS

The community is thriving, with over 40,000 developers using their inference APIs. Model builders like Nous Research, creators of Hermes 3, host large language models on the platform, creating an interconnected ecosystem rather than a siloed network. With over a billion tokens processed daily, Hyperbolic is proving its potential, though challenges remain. If PoSP or HyperdOS encounter issues, trust could waver. Scalability is another test as demand grows, but so far, they’re delivering