Use Remote GPUs

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.