Letβs understand condition of GPU markets and how Aquanode contributes π
1) Demand for Cheap GPUs
With the growing AI era we already know of the huge demand for compute, every startup, agency, and enterprise wants to use AI to make workflows faster and smarter. But not everyone can afford to rent an H100 GPU at $13.52/hour on AWS.
2) Incoming GPU availability crisis
Most platforms offering βcheap GPUsβ like
@runpod,
@TensorDock @vast_ai @Hyperstackcloud @FAL are using centralized distributed compute networks to break the price against hyperscalers like AWS, Azure etc. This puts a cap on scale and availability of GPUs.
3) DePIN Compute
Decentralized compute networks like
@akashnet @spheron @ionet @rendernetwork @gpunet @bittensor_ @Theta_Network solve this. They tap into a global pool of underused GPUs, anyone can contribute their machine and earn, while staying in full control of it. This sounds good and scalable.
4) But this has a problem
No custom runtimes, no persistent environments, and definitely no Docker-in-Docker β most DevOps stuff just wonβt work here. DePIN compute fixes the GPU availability issue, but you donβt get root access. Youβre basically working inside container VMs, which means no running Docker or K8s inside, no messing with system settings, no mounting things like sshfs or gcsfuse, and no launching full VMs or GUIs. You can type sudo, but itβs not real root.
5) Aquanode tries to make this compute usable
We create a usable environment on top of a DePIN-sourced compute using containerOps. Our focus is on the cheap GPU niche abstracting the complexities of decentralized infrastructure and making it easy for anyone to run and manage their workloads. By introducing features like serverless GPUs, real-time monitoring, and seamless workload management, we lower the entry barrier and unlock the true potential of distributed compute networks.