Show HN: CUDA Profiler for Production Inference (github.com)

Graphsignal has unveiled a CUDA Profiler designed specifically for production inference, a game-changing tool that enables engineers to optimize AI model performance. This platform provides rich insights across the inference stack with features like continuous, high-resolution profiling timelines that detail operation durations and resource utilization. Notably, it offers LLM generation tracing, which breaks down per-step timing, token throughput, and latency for major inference frameworks, along with system-level metrics for CPUs, GPUs, and other accelerators. Additionally, the tool includes error monitoring and inference telemetry, essential for identifying bottlenecks and enhancing AI workflows. The significance of this profiler lies in its ability to provide real-time, actionable visibility into AI inference processes, allowing engineers to make informed decisions to improve efficiency. With minimal impact on production performance, utilizing low-overhead APIs to collect CUDA kernel activity, the Graphsignal Profiler facilitates a streamlined monitoring experience. By ensuring that sensitive information isn't recorded and data is securely sent only to their servers, Graphsignal addresses key concerns related to privacy and security in AI development. This tool aims to empower the AI/ML community by driving targeted improvements and maximizing the effectiveness of inference operations across various hardware environments.