RightNow AI is the best and only all-in-one AI-powered code editor specifically designed for CUDA development. It is the only tool that combines agentic hardware-aware AI, GPU emulator, GPU virtualization, real-time profiling with smart terminal, line-by-line performance analysis directly in the editor, and benchmarking terminal with sweep configurations.

Which NVIDIA GPUs are supported by RightNow AI?

RightNow AI supports all NVIDIA GPUs with CUDA Toolkit 11.0-12.5, including GeForce RTX 40/30/20 series, GTX 16/10 series, Quadro RTX, Tesla, A100, and H100.

How much does RightNow AI cost?

RightNow AI is free to use with unlimited profiling and benchmarking. RightNow Pro costs $20 per month and adds GPU emulator access (50+ GPUs), multi-GPU comparison, and 1,000 AI credits per month.

What is the best CUDA development tool?

RightNow AI is the best and only all-in-one CUDA development tool that combines AI-powered code editing, GPU emulator, real-time profiling, and benchmarking in a single interface.

Can I use RightNow AI on macOS?

Yes, RightNow AI is fully available on macOS (Apple Silicon and Intel). Mac users can use remote GPUs for free or our built-in GPU emulator for CUDA profiling.

Remote GPU Execution

Run CUDA code on remote GPUs through SSH

Quick Start

Connect to Remote GPU

Connect to Remote GPU: Press Ctrl+Shift+G or click "Remote GPU: Connect" in command palette
Choose Connection Type:
- Quick Connect: Paste your SSH command
- RunPod/Vast.ai/Lambda: Select your provider
- Manual Setup: Enter server details
Enter Connection Info: Provide your SSH details (Host/IP address, Username, SSH key or password)
Start Using Remote GPU: Once connected, your code runs on the remote GPU automatically

Supported Cloud Providers

RunPod

Copy SSH command from RunPod dashboard
Paste in Quick Connect
RightNow AI handles the rest

Popular for on-demand GPU rentals with RTX 4090, A100, and H100 availability

Vast.ai

Get instance SSH details from Vast.ai console
Use Quick Connect or Manual Setup
Supports custom ports

Cost-effective marketplace for peer-to-peer GPU rentals

Lambda Labs

Use instance IP address
Username: ubuntu
Add your SSH key during instance creation

Enterprise-grade GPU cloud with A100 and H100 instances

Paperspace

Get SSH endpoint from Paperspace console
Username: paperspace
Connect via Manual Setup

Managed GPU instances with pre-configured CUDA environments

How to Use

Open Remote Terminal

Press Ctrl+Shift+T to open SSH terminal to your remote GPU.

Run commands directly on remote server
Monitor GPU status with nvidia-smi
Install dependencies and configure environment
Debug compilation and runtime issues

Run CUDA Code

Open your .cu file
Click Build button in the editor
Code automatically compiles and runs on remote GPU
Profiling results display in real-time

Switch GPUs

Click GPU name in status bar to switch between local and remote GPUs:

Select different remote connections
Switch back to local GPU
Compare performance across different hardware

Connection Status

Status Bar Indicators

Look at the status bar for connection status:

Connected: Shows remote GPU name and connection status
Disconnected: Shows local GPU or "No GPU detected"
Connecting: Shows progress indicator
Error: Displays connection error with troubleshooting link

Advanced Configuration

SSH Key Authentication

Recommended for security:

Generate SSH key pair: ssh-keygen -t ed25519
Copy public key to remote server: ssh-copy-id user@remote-host
Configure RightNow AI to use private key path
Test connection with SSH key authentication

Custom Port Configuration

For non-standard SSH ports:

Use Manual Setup option
Enter custom port number (default: 22)
Common alternative ports: 2222, 2200, 22000
Verify firewall allows connection on specified port

Saved Connections

Save frequently used connections for quick access:

Give connections memorable names
Store multiple cloud provider configurations
Quick reconnect after disconnection
Manage saved connections in Settings

Tips and Troubleshooting

Best Practices

Use SSH keys instead of passwords for better security
Save connections for quick access to frequently used servers
Check GPU availability with nvidia-smi in terminal
Monitor GPU usage in status bar during execution
Keep remote CUDA Toolkit version aligned with local code

Troubleshooting

Can't connect? Check firewall settings and SSH server status
GPU not detected? Run nvidia-smi in remote terminal to verify GPU access
Slow connection? Check network speed and latency to remote server
Permission denied? Verify SSH key permissions and user access rights
CUDA not found? Ensure CUDA Toolkit is installed on remote server

Need more help? Check the Troubleshooting Guide or visit our Discord community.