The Economics of Runtime Tokens

As models get capable enough to run whole tasks in production, the tokens that do it are paid on every execution, bounded by a latency or throughput budget, and never amortized. Those constraints decide where and how inference has to run.

Auto-Optimizing Inference with GPU Profiling and Telemetry

auto-flags sets inference-engine startup flags for you, chosen by Graphsignal's optimization agent from your workload's GPU profiling and telemetry - the config that fits your traffic, not a generic default.

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.

autodebug: Telemetry-Driven Inference Optimization Loop

An autonomous agent that deploys inference services, collects telemetry, and continuously redeploys with better configurations - indefinitely.

AI Debugging and Optimization For Production Inference

A practical workflow to debug production inference issues and optimize performance using Claude Code and Graphsignal debug context.

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.

LLM API Latency Optimization Explained

Learn how to make your LLM-powered applications faster.