One GPU out of 16,000 is 15% slow and the whole run is 15% slow. How do you find it, and what is usually wrong with it?

Every collective ends when the last rank arrives, so a single throttled GPU taxes 16,383 others. The arithmetic of that tax, the per-rank timing that finds the rank in one step, and the ranked list of causes from a hot GPU to a NIC with symbol errors, each with the command that confirms it.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

20 answers per topic instead of 10, plus saved progress and bookmarks · no cardor unlock all 283 remaining answers · ₹2,000 / $25

The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Advanced

🩺 Fleet Reliability & Observability🔒 Premium

Stragglers and HangsSynchronous training runs at the speed of its slowest rank, so one GPU that is 30% slow makes a thousand GPUs 30% slow, and one rank that never arrives at a collective makes the other 1,023 wait in silence until a watchdog fires ten minutes later. Finding the slow rank and the stuck rank is the most common on-call task on a training fleet, and the tooling for it (per-rank timing, the NCCL flight recorder, stack dumps across ranks) is specific and learnable. This page derives the straggler tax from first principles, lists the causes in the order they actually occur, and gives the procedure for a hang.

Foundational

🔌 Networking & Storage

Debugging a Slow All-ReduceA training job reports its all-reduce at a third of what the fabric should deliver, every node passed its health check, and nothing is logged. This page is the isolation order that finds the cause in an hour instead of a day: measure the collective in isolation, split the job until the slow pair or rank appears, then check the specific things that make a link, a node or a placement slow. Most cases end at one NIC, one topology mismatch, or GPUDirect silently off.

Foundational

🖧 Hardware & Cluster Build-Out

Cluster Bring-Up: Firmware, Drivers and the StackBring-up is an ordered dependency chain and skipping a step produces a symptom that points somewhere else. Firmware first, then the operating system and kernel, then the GPU driver, then the fabric manager, then the network stack, then GPUDirect, then CUDA and NCCL, then the container and scheduling layer. On Blackwell HGX systems the fabric manager reaches the NVSwitches through a bridge device and therefore depends on the InfiniBand stack being present, which is a dependency that surprises almost everyone the first time.

Core

🩺 Fleet Reliability & ObservabilitySign in

DCGM and GPU TelemetryNVIDIA's Data Center GPU Manager reads a GPU's counters, runs its diagnostics and exports both to the monitoring stack, and nearly every fleet's dashboards and alerts are built on it. The skill is knowing which of its hundreds of fields carry signal: the profiling metrics that say whether the tensor cores are busy (not the utilization number everyone reads first), the error counters that predict a failure, the throttle reasons that explain a slow step, and the diagnostic levels that decide whether a node returns to the pool. This page walks those fields, derives an MFU estimate from them, and gives a fleet's alert thresholds.

Explore the full curriculum →

UP NEXT ON YOUR JOURNEY

Next in this trackYou see 'Watchdog caught collective operation timeout' in the logs. What happened, and how do you find which rank caused it?Next in this trackHow do you overlap communication with compute in training, and where does the overlap stop working?Next in this trackHow is training on TPUs with JAX different from training on GPUs with PyTorch? What do you stop doing by hand?

FEDITOR'S NOTE

Scored on explaining why synchronous training turns one slow rank into a fleet-wide loss, on instrumenting per-rank compute time rather than reading NCCL time, and on a cause list with a check for each.

DISCUSSION · 0

No comments yet — be the first to share your approach.