Decentralized Physical Infrastructure Networks have evolved from a niche concept into a multi-billion dollar sector, with the total DePIN market valuation surpassing $50 billion. For technically proficient crypto enthusiasts, running a DePIN node represents an opportunity to earn passive income while contributing to the decentralized infrastructure that underpins the next generation of Web3 applications. This advanced tutorial walks through the complete process of setting up and optimizing a DePIN compute node.
The Objective
This tutorial guides you through deploying a GPU-equipped DePIN compute node on the Akash Network, one of the most mature decentralized cloud computing platforms. By the end, you will have a running node that provides computational resources to the network, earns AKT tokens for its contributions, and is configured for optimal performance and reliability. We assume familiarity with Linux command line, Docker, and basic blockchain concepts.
The Akash Network operates as a decentralized marketplace for compute resources. Providers list their hardware capabilities and pricing, while tenants deploy workloads through on-chain bids. The network has demonstrated 80% GPU utilization rates across its provider fleet, indicating genuine demand for decentralized compute. With AI training and inference workloads growing exponentially, the demand for distributed GPU compute is only increasing.
Prerequisites
Hardware requirements start with a dedicated GPU. An NVIDIA RTX 4090 or A100 is ideal for AI workloads, though an RTX 3090 or even an RTX 3080 can be viable for less demanding tasks. You need at least 32GB of system RAM, 500GB of NVMe storage, and a reliable internet connection with at least 100 Mbps symmetric bandwidth. Your hardware must be in a location with stable power and cooling—uptime directly impacts your earning potential.
Software prerequisites include a fresh Ubuntu 22.04 LTS installation, Docker Engine with the NVIDIA Container Toolkit, the Akash CLI tool, and a funded wallet with at least 5 AKT for the provider deposit. You will also need the Kubernetes command-line tool kubectl and the Akash provider services installed on your node. Ensure your GPU drivers are up to date—the NVIDIA 535 or later driver series is recommended for optimal container GPU passthrough.
Network configuration requires opening specific ports on your firewall. Akash providers need ports 8443, 8444, and a range of ephemeral ports for tenant workloads. Configure your router to forward these ports to your node and ensure your ISP does not block incoming connections on these ports. A static public IP address is strongly recommended—dynamic IPs will cause your provider to be intermittently unavailable.
Step-by-Step Walkthrough
Step 1: Install the Akash Provider Stack. Begin by adding the Akash repository and installing the provider services. Run the Akash provider setup script, which installs the Kubernetes distribution (k3s), configures the necessary namespaces, and deploys the provider agents. During setup, you will specify your GPU model, available RAM, and storage capacity. These specifications determine the pricing tier at which your node bids on workloads.
Step 2: Configure GPU Passthrough. This is the most critical step for compute nodes. Verify that your NVIDIA drivers are properly installed by running nvidia-smi. Then configure the NVIDIA Container Toolkit by registering it with Docker and restarting the Docker daemon. Test GPU passthrough by running a container with GPU access and verifying that nvidia-smi works inside the container. If this fails, revisit your driver installation and Container Toolkit configuration.
Step 3: Create Your Provider On-Chain. Using the Akash CLI, create a provider configuration file that specifies your node’s attributes including GPU count and model, CPU cores, RAM, storage, and your pricing preferences. Set your pricing in terms of AKT per GPU-hour, considering both your hardware costs and electricity expenses. A competitive but profitable price point for an RTX 4090 is typically between $0.40 and $0.80 per GPU-hour.
Step 4: Register and Bid. Submit your provider configuration to the blockchain using the provider create transaction. This requires the AKT deposit, which serves as a commitment guarantee. Once registered, your provider will automatically bid on workloads that match your specifications. The bidding process is handled by the provider agent, which evaluates incoming lease requests and places competitive bids based on your configured pricing strategy.
Step 5: Monitor and Optimize. Deploy monitoring using the Akash provider dashboard or set up Prometheus and Grafana for custom metrics. Track your utilization rate, earnings, and any failed lease attempts. If utilization is below 60%, consider lowering your pricing or expanding your supported workload types. If utilization is consistently above 90%, you may have room to increase prices.
Troubleshooting
GPU not detected in containers: This is the most common issue. Verify that nvidia-container-toolkit is properly installed and registered with Docker. Check that your container runtime configuration includes the NVIDIA runtime. Run docker info and confirm that nvidia appears in the available runtimes list. If using Kubernetes, ensure the device plugin for NVIDIA GPUs is properly deployed in your cluster.
Provider not receiving bids: Check that your pricing is competitive by comparing against other providers with similar hardware on the Akash network explorer. Verify that your provider attributes accurately reflect your hardware capabilities—an overstatement will lead to failed leases while an understatement means you miss out on workloads you could handle. Ensure your node is accessible by checking firewall rules and port forwarding configuration.
High lease failure rate: This typically indicates that workloads are failing to start on your node, often due to resource constraints. Monitor your system resources during lease startup to identify bottlenecks. Insufficient RAM is the most common cause—ensure you have enough headroom above the committed resources to handle the overhead of the container runtime and provider services themselves.
Mastering the Skill
Once your basic node is running smoothly, consider expanding your capabilities to attract higher-value workloads. Adding persistent storage support allows tenants to run database and stateful application workloads, which command higher prices than ephemeral compute. Implementing IP Leasing enables your node to provide dedicated IP addresses to tenants, a feature required for certain types of web hosting and API services.
Advanced operators should explore multi-node deployments, where a single provider manages multiple physical machines. This allows you to offer larger aggregate resources and attract workloads that require distributed compute across multiple GPUs. The Akash provider software supports this architecture through its inventory management system, which presents all your hardware as a unified resource pool.
Stay engaged with the DePIN community through Discord channels, governance forums, and provider operator groups. The DePIN landscape is evolving rapidly, with new networks, workload types, and optimization techniques emerging regularly. Operators who stay current with best practices and adapt their configurations accordingly will consistently outperform those who set and forget their nodes.
Disclaimer: This article is for educational purposes only and does not constitute financial or technical advice. Running infrastructure nodes involves costs and risks. Always conduct your own research and consider your circumstances before deploying any infrastructure.
Running a GPU DePIN node sounds interesting but I’m worried about the electricity costs eating into those AKT rewards
^ this. Also make sure your hardware is energy efficient. The 3090 I’m using costs less to run than my older 3080
Electricity is definitely a factor. I’ve found that running during off-peak hours makes a huge difference in profitability