The dominance of centralized AI systems is starting to give way to a new frontier: decentralized machine learning. Projects like Bittensor highlight a growing shift in how AI models are trained, deployed, and monetized. At the heart of this transformation lies a critical question: Can our infrastructure keep up?

Traditionally, AI has been controlled by a handful of companies with access to massive datasets and proprietary infrastructure. These centralized models pose challenges:

• Lack of transparency
• Limited access to training resources
• Concentration of power and control

Bittensor decentralizes model training and validation across thousands of nodes. It turns AI development into a permissionless, token-incentivized process. Instead of one company owning the model, the intelligence is distributed—and so are the rewards.

Decentralized AI introduces new infrastructure needs:

• Low-latency GPU compute to handle real-time inference
• Scalable provisioning for training models across subnets

High uptime and reliability to avoid penalties or slashing

ionstream is purpose-built for these demands:

• Our B200 and H200 GPUs deliver massive parallelism and bandwidth
• Bare-metal deployments eliminate virtualization overhead
• API-first provisioning lets you scale across multiple subnets quickly

We believe infrastructure should not be a barrier to decentralized innovation—it should be an enabler.

Ready to decentralize your AI training? Our infrastructure is optimized for both traditional and frontier use cases like Bittensor.Learn more