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.

Real-Time Profiling

Production-grade GPU profiling with NVIDIA Nsight Compute integration

NVIDIA Nsight Compute Integration

Production-grade profiling using nv-nsight-cu-cli with comprehensive hardware metrics

Core Performance Metrics

SM Efficiency: Streaming Multiprocessor utilization percentage
Memory Throughput: Achieved vs theoretical memory bandwidth (GB/s)
Occupancy: Active warps vs maximum theoretical warps
Warp Efficiency: Percentage of active threads in executed warps
Instruction Replay Overhead: Pipeline stall analysis
Global Memory Efficiency: Coalesced memory access patterns
Shared Memory Efficiency: Bank conflict analysis
Branch Efficiency: Divergent execution measurement

Advanced Metrics

L1/L2 Cache Hit Rates: Memory hierarchy performance
Register Usage: Per-thread register consumption
Power Draw: Real-time GPU power consumption (watts)
Temperature: GPU thermal monitoring
Roofline Analysis: Compute vs memory-bound classification

Multi-Level Profiling Support

Kernel Profiling

Profile specific __global__ functions with targeted analysis

Application Profiling

Full executable profiling with complete call graphs

CLI Integration

Direct nv-nsight-cu-cli integration with custom metrics

Visual Profiling Interface

CodeLens Integration

Inline performance metrics displayed above CUDA kernels with real-time execution time, SM efficiency, and memory throughput.

Color-coded performance indicators:

Green

>80% efficiency (optimized kernels)

Orange

40-80% efficiency (moderate performance)

Red

<40% efficiency (needs optimization)

Interactive Profiling Controls

Gutter Play Buttons: One-click profiling from editor margins
Dedicated Profiling Panel: Comprehensive results view with historical data
Multi-GPU Support: Device switching and cross-GPU analysis
Elevated Profiling: Windows UAC support for performance counter access

Line-by-Line Performance Analysis

Beyond kernel-level metrics, RightNow AI provides per-line profiling showing execution time, instruction count, and performance hotspots at source code granularity.

Per-Line Metrics

Execution Time: Time spent executing each source line
Instruction Count: Number of SASS instructions generated per line
Cycles per Line: GPU clock cycles consumed by each line
Memory Accesses: Global/shared memory operations per line
Warp Divergence: Branch divergence at instruction level

Hotspot Identification

Color-Coded Lines: Hot lines highlighted in editor gutter
Threshold Filtering: Only show lines exceeding performance threshold
Inline CodeLens: Performance metrics displayed above hot lines
Click-to-Navigate: Jump directly to expensive code sections

Smart Line Toggle: After kernel profiling completes, click "Show Line Profiling" to toggle per-line metrics view in editor.

AI-Powered Performance Analysis

Intelligent Optimization Recommendations

Bottleneck Classification: Memory-bound vs compute-bound identification
Architecture-Specific Suggestions: Tailored for detected GPU architecture
Performance Trend Analysis: Historical optimization tracking
Automated Code Suggestions: AI-generated kernel optimizations based on profiling data
Iterative Optimization: Multi-round autonomous optimization with correctness verification

Learn more: See CUDA Setup to configure profiling, Advanced Features for profiling data persistence, and Agentic AI Optimization for iterative kernel optimization.