io.net

Developing cutting-edge AI and machine learning applications demands significant computational power, often at prohibitive costs on traditional cloud platforms. io.net delivers the Internet of GPUs: a decentralized network providing cost-efficient, scalable compute specifically designed to fuel innovation for AI startups and ML teams.

Key Features

Here are the core capabilities io.net offers to empower your AI and ML development:

• 🌐 Massive Decentralized GPU Network: Access a global pool of underutilized GPUs sourced from independent data centers, crypto miners, and consumer hardware. This network provides access to potentially 200+ ExaFLOPS of compute, dramatically expanding available capacity beyond the 10-15 ExaFLOPS typically found on major clouds, directly addressing the GPU shortage.

• 💰 Significant Cost Savings: Leverage compute resources for up to 90% less than traditional cloud providers. By tapping into distributed, underutilized supply, io.net makes high-performance GPU clusters dramatically more accessible and affordable for demanding AI/ML workloads and iterative development.

• ⚡ Ultra-Fast Deployment: Bypass lengthy procurement processes, KYC requirements, and hardware waitlists. io.net allows you to deploy custom compute clusters in under 90 seconds, enabling rapid experimentation, development, and scaling of your applications.

• 🧠 Optimized for AI/ML Workloads: Built natively on Ray.io, the distributed computing framework used by leading AI labs like OpenAI, io.net provides a production-ready environment. It seamlessly supports popular ML frameworks such as PyTorch FSDP and TensorFlow for efficient distributed training, tuning, and model serving across large GPU clusters.

Use Cases

See how io.net can be applied to accelerate your specific AI and ML initiatives:

• Accelerating Large-Scale Model Training: Quickly provision vast clusters of GPUs to drastically reduce the time and expense involved in training complex deep learning models that would be cost-prohibitive or face long wait times on traditional infrastructure.

• Efficient Hyperparameter Tuning & Experimentation: Launch hundreds or thousands of parallel jobs across flexible CPU and GPU configurations to rapidly explore model architectures and hyperparameter spaces, accelerating your path to optimal model performance.

• Powering Complex Simulations & Reinforcement Learning: Utilize tailored CPU and GPU resource mixes to execute resource-intensive simulations required for areas like reinforcement learning or complex scientific modeling with greater speed and cost-efficiency.

Why Choose io.net?

io.net stands apart by fundamentally changing how you access and utilize GPU compute:

• Dramatic Cost Reduction & Speed to Deployment: Gain a significant competitive edge with access to high-performance compute at a fraction of the cost and the ability to deploy clusters in seconds, not weeks.

• Access a Global Pool of Underutilized GPUs: Tap into a vast, decentralized network providing access to compute capacity far exceeding traditional cloud offerings, mitigating GPU shortages and ensuring resources are available when you need them.

• Engineered for Distributed Machine Learning: With native Ray.io integration and support for key ML frameworks, io.net is specifically optimized to handle complex, distributed AI/ML workloads efficiently from day one.

Conclusion

io.net redefines access to high-performance compute, offering AI/ML teams the speed, scale, and cost efficiency needed to push the boundaries of innovation. By leveraging a decentralized global network, io.net provides a powerful, flexible, and accessible alternative to traditional cloud infrastructure.

Explore how io.net's decentralized GPU cloud can accelerate your AI development.

FAQ

Get clear answers to common questions about io.net:

• How is io.net cheaper and faster than traditional providers like AWS? io.net offers significant cost savings (up to 90% less) by aggregating underutilized GPU capacity from diverse sources outside traditional data centers. It's faster because deployment bypasses the typical hurdles of traditional cloud providers (KYC, long-term contracts, waitlists), allowing you to deploy compute clusters in under 90 seconds.

• How is io.net different from traditional cloud providers like AWS? io.net operates as a decentralized, distributed network (DePIN) rather than a centralized cloud. This model provides users with increased control, flexibility, and access to a much larger potential pool of hardware. It focuses specifically on high-performance compute for AI/ML and offers distinct advantages in cost, speed, and access to diverse hardware types.

• Why is io.net particularly suited for machine learning workloads? io.net is natively built on Ray.io, a leading open-source framework specifically designed for building and scaling distributed AI and Python applications. This integration provides a production-ready environment optimized for distributing common ML tasks like training, tuning, and model serving across large GPU clusters, supporting popular frameworks like PyTorch FSDP and TensorFlow out-of-the-box.

• How does io.net ensure data privacy and security? Security is a multi-layered approach. An IO agent is used to detect and block unauthorized containers. Data exchanged between worker nodes within the hired Docker file system is encrypted. Network traffic utilizes a secure mesh VPN. Additionally, io.net prioritizes suppliers who meet security compliance standards like SOC2, and the platform avoids using the --privileged flag for Docker containers to limit container access.