RightNow AI

Product Introduction

1. RightNow AI is an automated performance optimization platform for CUDA developers that profiles GPU kernels, identifies bottlenecks, and generates optimized code without requiring manual programming or deep technical expertise.
2. The core value lies in its ability to accelerate CUDA kernel execution by 2-4x through AI-driven analysis, architecture-specific tuning, and serverless profiling, enabling engineers to focus on high-level development rather than low-level optimizations.

Main Features

1. The AI Kernel Generator automatically rewrites CUDA kernels using natural language prompts, producing implementations optimized for specific NVIDIA architectures (Ampere, Hopper, Ada Lovelace, Blackwell) that outperform manual coding efforts.
2. Serverless GPU Profiling allows users to test kernels on cloud-hosted NVIDIA GPUs without local hardware, providing detailed performance reports and bottleneck analysis through a web interface.
3. Inference-Time Scaling dynamically adjusts computational resources during model deployment, maintaining optimal performance across varying workloads while reducing operational costs.

Problems Solved

1. The platform addresses the complexity of manual CUDA optimization, which traditionally requires months of expertise to achieve peak GPU utilization and often results in suboptimal kernel performance.
2. It serves AI research teams, HPC developers, and machine learning engineers who need to accelerate CUDA-based workloads but lack specialized GPU programming skills or time for iterative tuning.
3. Typical scenarios include optimizing inference pipelines for computer vision models, accelerating scientific simulations, and eliminating memory bottlenecks in production-grade deep learning frameworks.

Unique Advantages

1. Unlike traditional tools like NVIDIA Nsight Systems or CUDA-MEMCHECK, RightNow AI combines automated code generation with hardware-aware optimizations tailored for specific GPU architectures, eliminating the need for cross-platform compatibility testing.
2. The integration of natural language processing allows users to describe kernel functionality in plain English, which the AI translates into performance-optimized CUDA code with automatic memory alignment and warp scheduling.
3. Competitive differentiation comes from measurable 20x speedups in production environments, serverless access to latest-generation GPUs (including Blackwell), and a pay-as-you-go pricing model that reduces upfront infrastructure costs.

Frequently Asked Questions (FAQ)

1. What exactly can your AI Kernel Generator do for my code? The AI analyzes existing CUDA kernels through static code analysis and runtime profiling, then regenerates optimized versions with improved memory coalescing, reduced register pressure, and architecture-specific instruction scheduling.
2. How much of a performance boost can I expect? Benchmarks show 2-4x faster execution compared to baseline implementations, with cases like matrix multiplication and attention mechanisms achieving 15-20x speedups through automated kernel fusion and shared memory optimization.
3. What's inference time scaling? This feature monitors real-time inference workloads and automatically selects optimal GPU configurations (CUDA stream parallelism, batch sizes, tensor core utilization) to maintain latency SLAs while minimizing compute costs.
4. Which NVIDIA GPUs do you support? Full optimization support is provided for Ampere (A100), Hopper (H100), Ada Lovelace (RTX 4090), and Blackwell architectures, with backward compatibility to Volta and Turing through automated kernel variant generation.
5. Do I need to know CUDA to use this? No, the platform enables users to generate production-ready CUDA code through natural language prompts or by uploading existing kernels for automated optimization, though CUDA knowledge helps interpret profiling results.