Deploy AI Workloads on Dedicated, Decentralized Compute — Instantly.

Skyops gives developers, researchers and teams the ability to spin up GPU-powered environments across a global decentralized network. Whether you’re running a training pipeline, inference task or rendering batch, you can tap into remote compute without managing physical infrastructure.

🔩 Resource Allocation Model

Each Skyops job runs inside an isolated containerized environment — ensuring full performance and security.

• 🔹 GPU Access
  Every task is granted exclusive access to one or more physical GPUs. No time-sharing. No virtual GPU splitting.

• 🔸 CPU Scaling
  CPU threads are provisioned in proportion to the number of GPUs used — with dynamic bursting based on available headroom.

• 🧠 RAM Management
  Memory is auto-assigned relative to workload class, with buffers for temporary peak usage when available.

• 💾 Disk Volume
  Disk storage is fixed at job initialization. Users define required size up front. Data is ephemeral unless mounted to persistent volumes.

• 📎 Shared System Resources
  Jobs also receive shared memory and I/O allowances aligned with GPU capacity to prevent bottlenecks.

⏳ Job Duration & Lifecycle

All tasks have a defined runtime based on user configuration (hourly, daily or fixed sessions). Jobs terminate automatically at expiration unless extended manually or via API (subject to availability of the same node profile).

🐧 Operating Environment

• Linux-based Containers
  All compute jobs are encapsulated in Docker environments, preloaded with drivers, CUDA and popular AI frameworks.

• Custom Images Supported
  You can launch jobs using public or private images from Docker Hub, GitHub Container Registry or your own private repo.

🚀 Launch Modes

Skyops supports multiple job initiation styles depending on user preference and technical depth:

• 🧱 EntryPoint / Args – Ideal for automation and command-line pipelines.

• 🔐 SSH Access – Get full terminal control via secure key-based login.

• 📒 Jupyter Notebook – Launch interactive Python environments for rapid prototyping and live monitoring.

⚙️ Designed for Every Use Case

• AI/ML Training & Inference
  Run transformers, diffusion models, fine-tuning or inference pipelines with full GPU acceleration.

• Data Science & Visualization
  Deploy notebooks, stream processing jobs or visual rendering tools without worrying about setup.

• One-Time Compute
  Only need GPUs for a few hours? No problem — spin up jobs instantly and shut down when done.

Skyops removes the friction of accessing raw compute. No provisioning. No cloud dashboards. Just remote GPU power, programmable from the CLI, API or UI — available when you need it, on your terms.