Inference observability monitors inference systems at millisecond granularity, exposing internal runtime and GPU behavior hidden by second-level metrics.
Traditional observability was built for request–response backends: seconds-long requests, coarse metrics, and SLIs tuned to human-scale latency. Inference engines are the opposite. They run at extreme throughput with dense communication—token generation, attention kernels, memory transfers, CUDA launches, and synchronization points execute thousands of times per second. At that granularity, second-level metrics aren’t just insufficient; they’re structurally wrong. You cannot represent sub-second behavior with one-second buckets. Graphsignal introduces inference observability: real-time, millisecond-level resolution and inference-specific metrics that match how inference systems actually run.
Inference operates on a fundamentally different time scale. A single one-second metric bucket can contain tens of thousands of kernel executions and internal function calls. Short stalls, scheduling jitter, memory contention, and synchronization bubbles are averaged away before they ever reach a dashboard. These are exactly the kinds of issues that cut throughput and blow out tail latency—yet they’re intermittent and workload-dependent, so they slip past SLIs, percentiles, and alert thresholds built for slower systems. Traditional observability isn’t “missing a few metrics”; it’s built for a different domain. Applying it to inference is like using a thermometer to debug a CPU: wrong instrument, wrong granularity, wrong signal.
Inference observability starts from a different premise. When systems execute the same internal paths at massive frequency, profiling data becomes statistical signal rather than noise. At this scale, high-frequency profiles can be aggregated into metrics without losing meaning. Resolution on the order of tens of milliseconds becomes the minimum viable window where internal behavior survives aggregation while remaining practical to collect and analyze. That’s the crossover point where micro-regressions, GPU idle bubbles, and runtime inefficiencies become visible—the same issues that never persist long enough to show up in second-level telemetry.
Instead of treating inference like a slow web service, inference observability monitors systems at their native execution granularity: internal runtime functions, inference engines, and CUDA activity, not just requests and hosts. As inference workloads push higher throughput and tighter latency budgets, observability has to move to the time scale where these systems actually execute. Inference observability is that shift.
Graphsignal provides first-class inference observability across the stack. Inference engines such as vLLM are instrumented end-to-end: profiling covers the engine, scheduler, KV cache, and attention hot paths; tracing captures generate spans with model and request metadata and token/latency counters. Frameworks like PyTorch are profiled at the operator and module level (e.g. linear layers, attention, distributed collectives, CUDA sync points) with CUDA memory metrics from the runtime. At the device layer, NVIDIA GPUs are monitored via NVML for utilization, memory, temperature, power, PCIe and NVLink throughput, and error indicators. With auto-instrumentation for these engines, libraries, and GPU metrics, you get a single observability surface from high-level inference requests down to kernel and hardware behavior.
See the Quick Start guide to enable inference observability across your inference stack. Follow us at @GraphsignalAI for updates.