OpenGPU Cloudverse
Begin with OGPU Cloudverse
Cloudverse is OpenGPU’s decentralized, cloud-native compute environment that allows users to seamlessly deploy workloads on distributed GPU infrastructure. It offers the flexibility of traditional cloud providers (like AWS or GCP), but with the cost-efficiency, transparency, and decentralization of a Web3-native system.
It is particularly optimized for compute-intensive workloads in fields like AI/ML, high-performance computing, scientific research, data science, and generative media.
Core Attributes:
🔗 Runs on a global network of GPU providers (individuals, data centers, edge nodes)
🚀 Designed to deliver sub-60s job deployment
💰 Metered billing via smart contracts (Cloud Payments)
🌐 Integrates wallet-native authentication (no user accounts needed)
📡 Supports real-time job streaming, logs, and output retrieval
Cloudverse turns the GPU economy into an open marketplace — programmable, scalable, and community-owned.
💳 Cloud Payments: Pay-as-You-Compute
Cloud Payments is the payment and metering layer within Cloudverse that powers trustless, on-chain billing. It ensures that users only pay for the exact amount of compute they consume, with smart contracts managing pricing, deposits, refunds, and payment settlement.
🌍 Supported Payment Options:
$OGPU – Native token (default and incentivized)
Stablecoins – $USDC and $DAI via cross-chain bridges (Polygon, Base)
Fiat – via integrated payment gateways (Stripe, Transak) — Coming Soon
🔁 Workflow:
User estimates job cost via the OpenGPU dashboard or CLI
Funds are temporarily locked in a smart contract (escrow)
Upon job success:
The GPU node is paid
Unused gas/credits are returned to user
Receipt + audit log is generated
Cloud Payments removes the need for centralized invoicing, prepaid credit systems, or trust in intermediaries.
🛠 Use Cases: What You Can Build with Cloudverse
Whether you're a solo ML researcher or a startup deploying production AI, Cloudverse has you covered:
🧠 Machine Learning / AI
Train LLMs or fine-tune small/medium-sized models
Deploy inference endpoints for generative AI (Chatbots, Stable Diffusion)
Experiment with custom model architectures in PyTorch or TensorFlow
🧪 Scientific Computing
GPU-accelerated simulations in physics, chemistry, genomics
Monte Carlo methods or fluid dynamics
GPU offloading for compute-heavy research
🎮 Real-Time Rendering & Media
Render high-fidelity scenes in Blender or Unity
Offload GPU rendering for 3D NFTs or game environments
Generate synthetic datasets for AR/VR
🔍 Data Science & Quant
Large-scale data analysis (Pandas, Dask, RAPIDS)
Backtest trading algorithms or run ZK-proof circuits
Natural language processing (e.g., vector embeddings, RAGs)
Cloudverse brings the infrastructure muscle to the open compute frontier.
🧭 How It Works (Expanded)
🔐 Step 1: Connect Wallet & Fund Cloud Wallet
Connect your crypto wallet (e.g., MetaMask, Phantom, Coinbase Wallet)
Deposit $OGPU (or stablecoins) into your Cloud Wallet, a non-custodial smart contract holding your compute credits
View your balance, active jobs, and staked $OGPU from the OpenGPU dashboard
🔄 Optional: stake $OGPU to receive priority access + discounts
🚀 Step 2: Launch a Compute Job
Choose GPU class: NVIDIA A100, RTX 4090, or tiered performance categories
Select runtime environment:
Pre-configured: PyTorch, TensorFlow, CUDA, Jupyter
Custom Docker Image: Bring your own dependencies
Upload your code + data (via IPFS, GitHub, or direct upload)
Set job parameters (runtime, region preference, output location)
Cloudverse automatically matches your job to a node with compatible specs.
⚙️ Step 3: Metering + Payment Flow
Pre-bill: A prediction of job cost is generated using historical usage data
Escrow: Amount is held in a smart contract (time-locked)
Execution: The GPU node processes your job in a containerized sandbox
Completion:
Smart contract checks for successful hash/output signature
Fees are disbursed instantly
Audit logs are made available via IPFS or Arweave
Any unused funds are refunded
This approach guarantees fair usage, verifiable work, and zero hidden fees.
📊 Step 4: Monitor, Scale, Repeat
From the OpenGPU dashboard or CLI:
Monitor GPU usage (memory, compute cycles, cost)
Pause, resume, or clone workloads
Schedule repeating jobs or pipeline runs
Export logs, outputs, or performance reports for compliance
All jobs are containerized, stateless, and can be re-run from identical seeds.
Last updated