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.

Multi-DSL Support

Write GPU kernels in CUDA, Triton, CUTE, or TileLang with full IDE support

Supported Languages

RightNow AI supports four GPU kernel languages out of the box, giving you the flexibility to choose the right tool for your workflow:

CUDA

Native C++ for maximum control

Files: .cu, .cuh

Triton

Python DSL for rapid prototyping

Files: .py with @triton.jit

CUTE

NVIDIA CUTLASS templates for production GEMM

Files: .cu, .cuh with cute::

TileLang

Tile-based abstractions for readable code

Files: .py with @T.Kernel

Triton Support

OpenAI's Triton brings Python-level productivity to GPU programming. RightNow AI provides full IDE support:

Features

Syntax Highlighting: Full support for @triton.jit decorators and tl.* functions
Hover Documentation: 100+ function docs with examples (tl.load, tl.store, tl.dot, etc.)
Go-to-Definition: Jump to kernel definitions with one click
Semantic Tokens: Context-aware coloring for decorators, types, functions
NCU Profiling: Profile Triton kernels directly from the editor
AI Autocomplete: Context-aware suggestions for Triton APIs

Static Analysis Metrics

Real-time metrics displayed in CodeLens without execution:

num_warps - Number of warps per block
num_stages - Software pipelining stages
BLOCK_SIZE - Block size constant
Occupancy estimates and memory pattern analysis

Requirements: Triton must be installed in your Python environment (pip install triton)

CUTE Support

NVIDIA's CUTLASS/CUTE provides production-grade templates for matrix operations with Tensor Core support:

Features

Syntax Highlighting: Support for cute:: namespace and template syntax
Hover Documentation: 25+ function docs for layouts, tensors, and MMA operations
Go-to-Definition: Navigate to __global__ kernel definitions
CUTLASS Auto-Detection: Automatic discovery of CUTLASS installation
NCU Profiling: Full hardware profiling with Tensor Core metrics

Static Analysis Metrics

TILE_M, TILE_N, TILE_K - Tile dimensions
Pipeline stages for software pipelining
Shared memory tiling analysis
Tensor Core utilization hints

CUTLASS Setup

RightNow AI automatically detects CUTLASS from:

Environment variables: CUTLASS_PATH, CUTLASS_HOME
Common paths: C:\cutlass, /usr/local/cutlass
Project directory: ./cutlass/include

TileLang Support

Microsoft's TileLang provides clean, tile-based abstractions for GPU programming:

Features

Syntax Highlighting: Support for @T.Kernel and @tl.kernel decorators
Hover Documentation: 35+ function docs with usage examples
Go-to-Definition: Jump to kernel definitions
NCU Profiling: Profile TileLang kernels with real hardware metrics
AI Autocomplete: Context-aware suggestions for TileLang APIs

Static Analysis Metrics

block_size - Thread block size
tile_size - Tile dimensions
Sync point count and shared memory usage
Performance warnings for common issues

Requirements: TileLang must be installed in your Python environment (pip install tilelang)

Feature Comparison

Feature	CUDA	Triton	CUTE	TileLang
Syntax Highlighting	Full	Full	Full	Full
Hover Documentation	Full	100+	25+	35+
Go-to-Definition	Full	Full	Full	Full
Static Analysis	Full	Full	Full	Full
NCU Profiling	Full	Full	Full	Full
Benchmarking	Full	Full	Full	Full

Profiling All DSLs

All four DSLs support real NCU profiling directly from the editor:

How It Works

CUDA/CUTE: Compiled with nvcc and profiled with NCU
Triton/TileLang: Python wrapper profiling via ncu --target-processes all

Profiling Steps

Open your kernel file (any DSL)
Click the "Profile" CodeLens above your kernel
View real hardware metrics in the Profiling Panel

Learn more: See Real-Time Profiling for detailed profiling documentation and Static Analysis for instant metrics without execution.