How it works
Renting Compute on w.ai
Renting compute on w.ai provides a dedicated Jupyter Notebook environment backed by real GPU hardware from our distributed compute network. You gain full access to a sandboxed container, choosing between CUDA for NVIDIA GPUs, ROCm for AMD GPUs, or MLX for Apple Silicon, billed by the minute using w.ai points.
Step-by-Step Guide
Browse Available GPUs
Visit the Compute page at https://app.w.ai/compute to see live GPU inventory. Each card displays:
GPU Model — e.g., NVIDIA RTX 5090, Apple M4 Pro
VRAM — Total GPU memory available
Backend — CUDA, ROCm, MLX, or Vulkan
Price — Cost in w.ai points per hour
Availability — Number of workers offering this hardware
Use the search bar and category filters (NVIDIA, Apple, AMD, Intel) to find the right GPU for your workload.
Select Duration & Rent
Choose your rental duration from available options:
5–30 minutes
Quick experiments, testing code
1 hour
Standard development sessions
2 hours
Training runs, larger experiments
4–12 hours
Extended compute jobs
24 hours
Long-running training or processing
The UI shows your total cost before confirmation. Ensure you have sufficient w.ai points to cover the duration. Click Rent to confirm.
Wait for Provisioning
Upon confirmation, your rental enters a Pending state as:
The network matches a worker with your requested hardware.
The worker provisions an isolated container environment.
The Jupyter Notebook server starts and becomes accessible, typically taking under ~1 minute.
Access Your Jupyter Notebook
Once active, your rental card shows:
Session URL — Your unique Jupyter Notebook endpoint
Access Token — Authentication token
Time Remaining — Live countdown
Click Details or My Rentals for full access credentials, then Open Jupyter Notebook to launch your environment in a new tab.
Your Jupyter Environment
Each rental provides a fully configured Jupyter Notebook environment with Python 3.11, pip, and a comprehensive set of pre-installed packages.
Note: The environment is ephemeral. All files are deleted when the session ends. Download any results before your rental expires.
Pre-Installed Python Packages
Linux / Windows (CUDA & ROCm)
Notebooks
JupyterLab, ipywidgets, tqdm
Data Science
NumPy, Pandas, SciPy, scikit-learn
Visualization
Matplotlib, Seaborn, Pillow
ML / Deep Learning
PyTorch (CUDA/ROCm/CPU), TorchVision, TorchAudio, MLX-CUDA, Unsloth
Hugging Face
Transformers, Datasets, Hugging Face Hub, Accelerate, Diffusers, Safetensors
macOS (Apple Silicon)
Notebooks
JupyterLab, ipywidgets
Data Science
NumPy, Pandas, SciPy, scikit-learn
Visualization
Matplotlib
ML / Deep Learning
PyTorch (MPS), TorchVision
Hugging Face
Transformers, Diffusers, Accelerate, Safetensors
Apple MLX
MLX, mlx-lm, mlx-vlm, mlx-audio, mlx-video, MFlux
Base Container Images
Each environment is built on a minimal, production-grade base image depending on the backend:
CUDA (NVIDIA)
ROCm (AMD)
Vulkan
All images also include: curl, git, ca-certificates, glib, Python 3.11.14, and uv (fast Python package installer).
Need an additional system package? Contact us to request additions to the base image.
Managing Your Rentals
Visit My Rentals in the sidebar to manage all sessions.
Extending a Session
Need more time? Click Extend to add minutes or hours. Extensions charge the same per-minute rate and require a sufficient point balance.
Stopping Early
Click Stop Rental to end a session early. Billing is pro-rated by the minute — you are charged only for the minutes used and remaining reserved points are refunded.
Pricing
Rental pricing depends on:
GPU Performance — Higher-tier GPUs cost more points per hour
VRAM — More memory increases the base price
Network Earnings — Prices reflect actual GPU earning rates on the network
A minimum charge of 1 point per minute applies to all sessions. Pro-rated billing refunds unused reserved points if a session ends early. Prices are displayed on each GPU card in the Compute marketplace.
Networking Restrictions
For security, outbound network access from rental environments is restricted to an allowlist of trusted domains required for ML development:
pypi.org
Python package index
files.pythonhosted.org
Python package downloads
huggingface.co
Hugging Face Hub
hf.co
Hugging Face short URLs
download.pytorch.org
PyTorch packages
github.com
Git repositories
githubusercontent.com
GitHub raw content
gitlab.com
Git repositories
dl.fbaipublicfiles.com
Meta AI research assets
storage.googleapis.com
Google Cloud storage
All other outbound network traffic is strictly blocked. This includes general web browsing, SSH connections, and access to arbitrary external services.
Need access to an additional domain? Contact us on Discord to request additions to the network allowlist.
Security
w.ai compute sessions are designed with defense-in-depth isolation to protect both renters and workers.
Linux / Windows
All Linux capabilities dropped — containers run with the minimal possible privilege set
Non-root user — system package installation is disabled inside the container
Network isolation — outbound traffic restricted to the allowlist above; all other traffic denied
Resource limits — CPU, memory, and ulimits are enforced to prevent resource abuse
Container filesystem is ephemeral and fully destroyed on session termination
macOS (Apple Silicon)
Network isolation — very limited networking restricted to the allowlist above
Locked-down sandbox — all system access is blocked outside the sandbox directory
Restricted shell — only a very limited set of shell commands are enabled; all others are blocked
Minimal kernel access — only essential kernel services are available to the sandbox environment
Worker Requirements
To contribute your machine as a compute rental worker, you must meet these minimum requirements:
All Platforms
100 GB available storage — required for container images, models, and session data
Linux
System packages:
uidmap,iptablesNVIDIA GPUs additionally require:
nvidia-container-toolkit
Windows
NVIDIA GPU required
WSL (Windows Subsystem for Linux) must be installed
The w.ai worker will attempt to install WSL automatically if missing — a system reboot is required to complete setup
macOS
Apple Silicon (M1 or later) required
No additional system packages needed
Running the Worker
Start the worker in the foreground:
Or run in the background (detached mode):
Manage background workers with:
Workers earn w.ai points for every minute a rental session is active on their hardware.
Last updated