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.

GPU Emulation

Test CUDA code on different GPUs without owning the hardware

Quick Start

Getting Started with GPU Emulation

Open GPU Emulation: Click the GPU Emulation icon in the sidebar (circuit board icon)
Choose a GPU: Select the GPU you want to test from the list. Click the checkbox next to it
Run Your Code: Open your .cu file and click the Build button. Your code runs on the selected GPU

Available GPUs

The emulator includes all major NVIDIA GPUs across different generations:

Latest Generation

H100, H200
RTX 4090, RTX 4080
L40S

Data Center

A100, A10
V100
T4

Gaming & Workstation

RTX 3090, RTX 3080
RTX 2080 Ti
GTX 1080 Ti

Legacy Systems

Tesla K80
GTX 1060
Earlier architectures

How to Select GPUs

Selection Methods

Single GPU: Check one box to test on that GPU
Multiple GPUs: Check multiple boxes to compare performance across different architectures
Search: Type GPU name in search box to find it quickly
Clear: Click "Clear Selection" to deselect all GPUs

What You'll See

When emulation is active:

Status bar shows the selected GPU name
"Emulation Active" indicator appears
Performance results show estimated metrics
Architecture-specific warnings if code uses unsupported features

Best Uses

Development & Testing

Test code compatibility on different GPUs
Develop without expensive hardware
Compare performance across architectures
Learn CUDA on any computer

Architecture Exploration

Test Hopper features without H100 access
Evaluate tensor core performance
Understand compute capability differences
Plan for future GPU upgrades

Educational

Learn CUDA without GPU hardware
Experiment with different configurations
Understand architectural differences
Practice optimization techniques

Performance Analysis

Estimate performance on target hardware
Compare workloads across GPU generations
Identify architecture-specific bottlenecks
Validate optimization strategies

Emulation Features

Accurate Hardware Modeling

Compute Capability: Emulates correct CC version for each GPU
Memory Specs: Realistic memory bandwidth and capacity constraints
SM Count: Accurate streaming multiprocessor configuration
Register Limits: Per-thread and per-block register constraints

Performance Estimation

Execution Time: Estimated kernel runtime based on architecture
Occupancy: Calculate theoretical occupancy limits
Memory Throughput: Bandwidth and latency modeling
Bottleneck Analysis: Identify compute vs memory bound kernels

Compatibility Checking

Feature Detection: Warns about unsupported CUDA features
Architecture Warnings: Alerts for architecture-specific code paths
Compilation Validation: Ensures code compiles for target GPU
API Compatibility: Checks CUDA API version requirements

Tips and Limitations

Best Practices

Use emulation for compatibility testing and initial development
Compare performance trends across GPU generations
Validate architectural assumptions before hardware purchase
Test multiple GPUs to ensure broad compatibility

Important Limitations

Estimates Only: Emulation provides estimates. For production benchmarks, use real hardware
Performance Accuracy: Timing estimates are approximate and may vary from actual hardware
Real Hardware Preferred: Always validate critical optimizations on actual target GPUs
Architecture-Specific Features: Some advanced features may not be fully emulated

Next Steps: For real hardware testing, explore Remote GPU Execution to connect to cloud GPUs or your own remote servers.