RightNow AI 'V2.0'

Product Introduction

1. RightNow AI 'V2.0' is an AI-powered optimization platform designed to automatically profile, analyze, and enhance CUDA kernel performance for NVIDIA GPU architectures. It integrates machine learning-driven analysis with hardware-aware optimizations to deliver measurable speed improvements without requiring manual code tuning.
2. The core value lies in its ability to reduce development cycles by automating performance bottlenecks detection and generating optimized CUDA code, enabling engineers to focus on higher-level algorithmic improvements rather than low-level GPU tuning.

Main Features

1. The AI Kernel Generator produces CUDA kernels that outperform standard implementations by 2-4x through automated analysis of memory access patterns, thread block configurations, and instruction-level optimizations tailored to specific NVIDIA architectures.
2. Serverless GPU Profiling allows users to test kernels on cloud-hosted Ampere, Hopper, Ada Lovelace, or Blackwell GPUs without local hardware, providing detailed performance metrics like memory bandwidth utilization and warp stall analysis.
3. Natural Language Processing Engine interprets plain English prompts (e.g., "Optimize matrix multiplication for FP16 on Hopper") to generate production-ready CUDA code, eliminating the need for deep GPU architecture expertise during initial development.

Problems Solved

1. Eliminates weeks of manual performance tuning by automatically identifying and resolving CUDA kernel bottlenecks such as shared memory bank conflicts, inefficient coalesced memory access, and suboptimal kernel launch configurations.
2. Serves AI research teams, HPC developers, and machine learning engineers who require maximum GPU utilization but lack specialized CUDA optimization expertise or dedicated profiling hardware.
3. Accelerates critical workflows including real-time inference optimization for LLMs, physics simulation speedups, and computer vision pipeline enhancements where 20% latency reduction directly impacts operational costs.

Unique Advantages

1. Unlike traditional profilers like NVIDIA Nsight Systems, RightNow AI combines hardware telemetry with architectural-aware AI models trained on 10,000+ optimized kernel patterns across multiple GPU generations.
2. Patent-pending architecture switching enables cross-generation optimization, allowing users to benchmark kernels against future NVIDIA GPUs (e.g., Blackwell) before hardware availability.
3. Provides 24-hour ROI through its pay-per-optimization pricing model, contrasting with legacy tools requiring annual licenses costing $15k+/year without automated optimization capabilities.

Frequently Asked Questions (FAQ)

1. What exactly can your AI Kernel Generator do for my code? The system performs automated loop unrolling, shared memory partitioning, and warp scheduling optimizations while maintaining numerical accuracy, typically achieving 2-4x speedups over manually tuned CUDA kernels in benchmarks.
2. How much of a performance boost can I expect? Users report 3-5x acceleration for common operations like matrix multiplies and 15-20x improvements in memory-bound kernels through automated L1 cache configuration and tensor core utilization optimizations.
3. What's inference time scaling? The platform dynamically adjusts kernel parameters during deployment based on real-time input dimensions and batch sizes, maintaining <5% performance variance across different workload scales without recompilation.
4. Which NVIDIA GPUs do you support? Full optimization support for Ampere (A100), Hopper (H100), Ada Lovelace (RTX 4090), and pre-optimization profiling for Blackwell architecture, with backward compatibility to Volta (V100) through architecture emulation.
5. What's in the Pro plan? Includes 120 monthly optimizations, priority queue access for kernel generation, and multi-GPU comparative profiling across 4 architectures simultaneously for $20/month.
6. Do I need to know CUDA to use this? While basic CUDA understanding helps, the natural language interface and automated optimization enable users with Python-level GPU experience to generate production-grade kernels.
7. How do I get started? Upload existing CUDA kernels via WebAssembly-compiled sandbox or describe desired operations in plain English, with first optimization results delivered in under 90 seconds through browser-based IDE integration.