graphsignal-top: a live view of your inference GPUs

The open-source Graphsignal profiler serves everything it measures as JSON for agents. graphsignal-top is the same data as a live terminal screen: GPU telemetry, engine throughput and latency, and kernels ranked by time, locally or over the network.

Graphsignal vs. torch.profiler and nsys

torch.profiler and nsys already give you per-kernel GPU time, so what does Graphsignal add? Comparable JSON snapshots, telemetry in the same document, GPU probes, overhead low enough to run in production, and a runner that wraps any engine.

CUDA Profiler for Production Inference

Why dev-time CUDA profilers don't fit production inference, and what a profiler built for it looks like: low-overhead kernel attribution, host sync waits, and integrated telemetry.

Traditional Observability Is Blind to Inference

Inference observability monitors inference systems at millisecond granularity, exposing internal runtime and GPU behavior hidden by second-level metrics.

vLLM Production Observability: From Model to Hardware

Production-grade profiling and monitoring for vLLM: always-on vLLM, PyTorch and CUDA profiling with tracing, metrics and errors in one place.