Understanding Inference for Systems Engineers

A stage-by-stage walk through what actually happens when a large language model generates text, told through one real engine built for one model on one GPU: tokenization, the forward pass layer by layer, fp8 weights, prefill, CUDA graphs and megakernels, sessions, speculative decoding, grammar-constrained decoding, and correctness.

The Economics of Runtime Tokens

Tokens spent running tasks in production are paid on every execution and never amortize. That pushes this kind of work onto an open inference stack, where the bill is set less by the GPU-hour price than by how well you tune it to your own workload.

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.

LLM API Latency Optimization Explained

Learn how to make your LLM-powered applications faster.