Training AI models or running large-scale AI applications requires serious computing power, the kind most people and even small companies can’t afford to buy. Usually, you’d rent that power from big cloud providers like Amazon or Google. But what if your AI work involves sensitive data you don’t want anyone else to see?

Targon solves this problem by providing a marketplace for secure computing power. Built by Manifold Labs and running as Subnet 4 on Bittensor, Targon lets you rent powerful GPUs and CPUs for AI work while ensuring nobody, not even the people providing the computers, can access your data or models.

What Targon Actually Does

Targon is a decentralized marketplace for computing power, specifically designed for AI workloads that need to stay private. And here’s how it works. 

People with powerful computers (usually with high-end NVIDIA GPUs) offer their computing power for rent. Other people who need that power for AI training, running models, or other compute-heavy tasks can rent it. The whole process happens through Bittensor’s network, which coordinates who provides what and handles payments automatically.

The key difference from normal cloud computing is security. When you use Amazon Web Services or Google Cloud, your data and code run on their servers. You have to trust they won’t look at it, copy it, or leak it. With Targon, the system uses special hardware protections that make it physically impossible for the computer owner to access what you’re running.

This matters a lot if you’re working with proprietary AI models, sensitive training data, or confidential business information. You get the computing power you need without exposing your valuable intellectual property.

How the Security Actually Works

Targon uses something called Trusted Execution Environments, or TEEs. These are special secure areas built into modern computer processors like Intel TDX and AMD SEV.

Think of a TEE like a locked safe inside a computer. Your code and data go into this safe, and the safe does all the work. Even the person who owns the computer can’t open the safe to see what’s inside. The processor itself enforces this with hardware-level encryption.

For GPUs, Targon uses NVIDIA’s confidential computing technology with something called Protected PCIe. This encrypts data as it travels between the processor and the graphics card, so nobody can intercept it along the way.

The system also verifies that the hardware is legitimate before you use it. It checks that the processor has the right security features, that nothing has been tampered with, and that the environment is genuinely secure. This is called “attestation”; the hardware proves it’s trustworthy before you send your sensitive work to it.

All of this happens automatically in the background. You don’t need to understand the technical details. You just rent computing power, knowing it’s secured by the hardware itself, not just promises from a company.

What You Can Use It For

Targon is designed for AI workloads, but that covers a wide range of tasks.

Training AI models is the most obvious use. If you’re developing a machine learning model and need powerful GPUs to train it on large datasets, you can rent Targon’s computing power. Your training data and model architecture stay completely private throughout the process.

Running AI inference means using trained models to make predictions or generate outputs. If you have an AI model that needs to process lots of requests quickly, you can deploy it on Targon’s infrastructure. The model itself and whatever data people send to it remain confidential.

Testing and development is easier when you can rent computing power on demand. Instead of buying expensive GPUs that sit idle most of the time, you pay only when you need to test something that requires serious compute.

Peak usage handling works well with Targon because it auto-scales. If your AI application suddenly gets a lot more users, Targon can automatically add more computing resources to handle the load, then scale back down when traffic decreases.

The platform supports standard tools and APIs, including OpenAI-compatible endpoints. This means if you built something to work with OpenAI’s API, you can likely switch to Targon without major code changes.

Who Provides the Computing Power

The computers you rent on Targon come from individual miners, people or companies who own GPUs and CPUs and want to earn money by renting them out.

These aren’t random home computers. Miners typically have high-end hardware like NVIDIA H200s or RTX 4090s, professional-grade equipment that costs thousands or tens of thousands of dollars. Some are individuals who invested in powerful gaming or mining rigs. Others are small data centers with racks of GPUs.

Miners earn rewards in two ways. They get paid directly by users who rent their computing power, and they earn TAO tokens (Bittensor’s currency) and SN4 tokens (Targon’s specific token) based on how much quality service they provide.

Validators on the network check that miners are actually doing what they claim. They verify performance, uptime, and that the security protections are working correctly. This ensures you get what you pay for and that the hardware is genuinely secure.

The whole system runs automatically through Bittensor’s incentive mechanisms. Good miners who provide reliable, secure computing get more rewards. Bad miners who have poor performance or security issues earn less and eventually get pushed out.

The Performance Numbers

Targon claims over 1,000 GPUs and CPUs available across their network. They report 99% uptime, meaning the service is available almost all the time. Response times are under 50 milliseconds, which is fast enough for real-time AI applications.

These numbers matter because reliability is crucial when you’re running production AI systems. If your service depends on rented computing power, that power needs to actually be there when you need it.

As Subnet 4 on Bittensor, Targon earns roughly ~1.23% of the network’s emissions, which translates to around ~297 TAO per day in revenue. This makes it one of the more successful subnets economically, suggesting real demand for what they provide.

Recent performance for SN4 token holders has been modestly positive in the very short term, with recent gains of around ~13% over the past week. The token currently trades around ~$10, with a market cap of about ~$40 million.

How Regular People Can Participate

Most people won’t directly rent computing power from Targon, that’s mainly for developers and companies building AI applications. But there are other ways to participate.

If you own high-end GPUs, you can become a miner and rent out your computing power. This requires technical setup and staking some TAO tokens, but the guides and community can help. You earn money from rentals plus token rewards based on performance.

If you hold TAO tokens, you can stake them on Subnet 4 to earn rewards from the subnet’s success. This is passive, since you’re essentially betting that Targon will continue performing well and earning emissions.

If you’re building AI applications, you can actually use Targon’s services to rent secure computing power for your projects. Check their website at targon.com for pricing and how to get started.

The Broader Picture

Targon represents a specific vision for how AI infrastructure should work. Instead of a few giant companies controlling all the computing power and seeing all the data, computing resources come from distributed providers who can’t access what they’re processing.

This matters more as AI becomes critical infrastructure. Companies developing proprietary AI models can’t afford to expose their intellectual property. Healthcare organizations can’t send patient data to cloud providers without strict protections. Financial services need confidentiality for algorithmic trading strategies.

Traditional cloud providers address this with contracts and trust. Targon addresses it with hardware-level security that doesn’t require trust. The computer physically cannot access your data, regardless of who owns it or what they want to do.

Whether this approach scales to compete with Amazon and Google remains to be seen. Decentralized systems add complexity. Coordinating thousands of individual miners is harder than managing centralized data centers. Quality control is more challenging when you don’t directly control the hardware.

But the fundamental value proposition is real: secure, decentralized computing where your sensitive work stays genuinely private. For use cases where that matters, Targon offers something centralized providers can’t match; mathematical proof of privacy rather than promises.

Targon has been live since 2024, raised $10.5 million in funding in August 2025, and continues developing the platform. They’re adding features like secure model training and expanding support for different hardware types. The team is based in Austin, Texas, led by former Bittensor engineers who understand both the technical and organizational challenges.

For developers building AI applications with sensitive data, for companies that can’t risk exposing proprietary models, or for anyone who needs the guarantee that their computing work stays private, Targon provides a working solution right now.