RightNow AI Code Editor

Product Introduction

1. RightNow AI Code Editor is the first CUDA-native integrated development environment (IDE) specifically designed for NVIDIA GPU-accelerated programming. It combines real-time GPU profiling, AI-driven code optimization, hardware virtualization, and architecture-specific emulation into a unified workspace. The editor supports CUDA C/C++, PyTorch, and TensorFlow workflows while providing direct access to low-level GPU metrics during development.
2. The core value lies in eliminating disjointed toolchains by integrating performance analysis, AI-assisted optimizations, and hardware abstraction layers directly into the coding environment. This enables developers to write high-performance CUDA code with immediate feedback on GPU utilization, memory patterns, and kernel efficiency without context switching between separate profiling tools.

Main Features

1. Real-time inline profiling displays GPU metrics like SM occupancy, memory throughput, and warp scheduling efficiency alongside active code segments using NVIDIA Nsight Compute (NCU) integration. Developers can view cache hit rates (L1/L2/HBM), tensor core utilization percentages, and kernel duration metrics without leaving the editor.
2. Hardware-aware AI optimization automatically suggests architecture-specific improvements for GPUs like A100 (sm_86), H100 (sm_90), or consumer-grade RTX 4090 (AD102), including warp size adjustments, shared memory bank conflict resolutions, and optimal block/grid configurations based on live profiling data.
3. Full GPU virtualization emulates 86+ NVIDIA architectures through cycle-accurate simulation, allowing developers to test CUDA kernels on unsupported hardware like data center GPUs (L40S, H200) from local machines. The emulator supports mixed-precision operations, tensor core emulation, and memory hierarchy modeling up to 80GB HBM2e.

Problems Solved

1. Addresses the fragmented workflow of traditional CUDA development where engineers separately use NVProf, Nsight Systems, and manual benchmarking tools. The integrated environment reduces debugging cycles by 60-75% through synchronized code-profilng visualization.
2. Targets GPU software engineers, ML infrastructure teams, and researchers working on compute-intensive workloads like LLM training, HPC simulations, or real-time computer vision.
3. Enables performance validation across multiple GPU generations during code writing, automatic detection of suboptimal memory access patterns, and AI-guided optimization for specific architectures like Ampere or Hopper before deployment.

Unique Advantages

1. Unlike generic IDEs or standalone profilers, RightNow provides architecture-specific optimization prompts using LLMs trained on CUDA best practices for each GPU generation (Maxwell to Blackwell). This includes automatic FP16/FP8 conversion suggestions and tensor core utilization strategies.
2. Patent-pending GPU instruction set emulation allows accurate performance prediction for unreleased hardware (e.g., NVIDIA B100/B200) through architectural simulation, including SM count variations and memory subsystem changes.
3. Combines enterprise-grade features like multi-GPU benchmarking terminals, NCCL communication analysis, and Triton kernel optimization with local LLM privacy through BYOK (Bring Your Own Kernel) support for sensitive projects.

Frequently Asked Questions (FAQ)

1. Does RightNow AI require physical NVIDIA GPUs for development? The integrated GPU emulator enables full CUDA kernel testing without physical hardware, simulating architectures from consumer GeForce cards to data center A100/H100 using cycle-accurate models of streaming multiprocessors and memory controllers.
2. How does the AI optimization compare to manual CUDA tuning? The AI agent analyzes real-time profiling metrics (shared memory bank conflicts, register pressure, occupancy limits) and cross-references them with a knowledge base of 10,000+ optimized kernels, providing line-specific suggestions like loop unrolling factors or memory coalescing techniques.
3. What local LLM integrations are supported? RightNow natively integrates Ollama for private model serving, vLLM for tensor parallelism optimization, and allows custom model integration through ONNX runtime compatibility, all while keeping training data and kernels isolated from cloud services.
4. Can I benchmark across multiple GPU architectures simultaneously? The benchmarking terminal supports concurrent execution across 8+ connected GPUs (local or cloud-based) with automated A/B testing of kernel variants, generating CSV reports comparing metrics like TFLOPS, memory bandwidth utilization, and energy efficiency across architectures.
5. How does hardware-aware code generation work? When detecting GPU architecture (e.g., sm_90 for H100), the AI automatically adjusts warp synchronization strategies, suggests optimal thread block dimensions per SM count (132 SMs on H100), and enables architecture-specific intrinsics like Hopper’s TMA (Tensor Memory Accelerator) instructions through code completion.