aqua
duck
The tech industry is experiencing a global RAM shortage. GPUs are becoming more expensive and less available. Yet demand for AI inference is skyrocketing. Well, what if there was an inference provider unaffected by these factors and didn't rely on expensive infrastructure? What if there was a new type of infrastructure that used spare compute on people's idle devices to provide inference? Wouldn't that be cool? No large data centers, no expensive hardware—just pooled power from everyday devices. Welcome to an environmentally conscious AI infrastructure. Welcome to AQUADUCK.
Whether you're a provider of inference services or a company that supplies the critical hardware for it, physical compute resources are a bottleneck. Chip shortages, supply chain issues, and other factors have made it difficult to get the hardware you need when you need it. Not a problem here.
The network routes each request to the best available set of nodes, where the model is loaded and inference is executed across distributed compute without requiring you to manage GPUs yourself. This gives you scalable, resilient access to AI inference capacity for large models and high-volume workloads—without being locked into fixed cloud infrastructure.
A recent wave of agent-usage data suggests the real bottleneck isn't model quality, but token consumption at scale: one large empirical study of over 100 trillion real-world tokens found a clear rise in agentic inference, while newer coding-agent research shows simple repo instructions can cut runtime by 28.6% and output token use by 16.6%.
elastic global compute
We leverage the power and availability of decentralized compute to offer you a more affordable and flexible alternative to centralized GPU providers for high-volume agent and inference workloads.
The network routes each request to the best available set of nodes, where the model is loaded and inference is executed across distributed compute without requiring you to manage GPUs yourself. This gives you scalable, resilient access to AI inference capacity for large models and high-volume workloadswithout being locked into fixed cloud infrastructure.
With elastic, auto-scaling architecture, you never need to worry that there won't be enough capacity when you need it. The network will scale up to meet demand and scale down when there's less demand.
quack quack quack
Ideal for high-volume inference workloads, such as long-running agent tasks (e.g., OpenClaw), deep analysis of large datasets (e.g., research), and model training (e.g., pre- and post-training).
The network routes each request to the best available set of nodes, where the model is loaded and inference is executed across distributed compute without requiring you to manage GPUs yourself. This gives you scalable, resilient access to AI inference capacity for large models and high-volume workloads—without being locked into fixed cloud infrastructure.
A recent wave of agent-usage data suggests the real bottleneck isn't model quality, but token consumption at scale: one large empirical study of over 100 trillion real-world tokens found a clear rise in agentic inference, while newer coding-agent research shows simple repo instructions can cut runtime by 28.6% and output token use by 16.6%.
benefits
- No physical hardware
- GGUF model support
- MLX model support
- Low cost pricing
- Dynamic model routing
- Auto-scaling architecture
- Zero upkeep infrastructure
- Automatic node rebalancing
- Latency-aware node selection
- Mixture of Experts (MoE) sharding
- Always up-to-date developer docs