debugging

Compare the same benchmark on a healthy node, then follow the evidence through NCCL transport choices, device topology and fabric health. Half the measured bandwidth is a symptom, not a unique diagnosis.

Diagnose failed 800G links using endpoint compatibility, lane modes, inventory and controlled swaps. Learn why speed labels and failure patterns do not prove a cause.

Localize a slow fabric rail with matched tests, path comparisons and counter deltas. Separate an independent-bandwidth sum from collective completion time.

Diagnose a multi-node regression by comparing host and GPU transfer paths, active registration mechanisms and the tested software stack.

Fabric Manager helps make an NVSwitch system ready. Learn the platform-specific roles, failure symptoms and checks that prove the communication path works.

Reproduce the baseline, compare equivalent work, and trace the missing time. Acceptance, production and the original plan may differ in more than one way.

Confirm the event definition and exposure, compare cohort rates, then test whether the cause follows a module, location or optical path.

A few extra live values can change register allocation, residency, generated instructions or spills. Compare compiler evidence with runtime behavior before attributing a 30% slowdown to any one of them.

Longer sequences exercise more state updates, different boundaries and sometimes different kernel paths. Use a two-tile example to check online softmax, then test masking, indexing and precision against a stable reference.

Read a watchdog timeout without blaming the reporting rank. Compare group-scoped traces, separate missing evidence from mismatched calls, and distinguish CPU submission from GPU completion.

Trace the slow requests through queue, engine and gateway clocks. A flat median narrows the investigation, but it does not establish a cause.

Diagnose a stream that begins quickly but produces useful answer content late. Compare generation, parser and client clocks, then test a reasoning budget without hiding truncated or incorrect responses.

Find which startup step runs out of memory, count the allocations on that GPU, and change the setting that controls the shortage.

Find whether expert placement, small batches or communication makes the deployment slower. Compare equivalent workloads and inspect the worker that holds up each step.

The answers sound fluent, but the scores dropped. Compare inputs, generation settings, raw output, parsing and scoring to find where the new deployment differs.

The nodes joined the cluster, but the first request hangs. Trace how far it got, check worker assignments, and reproduce the blocked operation on the same communication path.

A fourfold ratio is a symptom, not a diagnosis. Trace individual requests, separate waiting from execution and delivery, and test workload, scheduling, cache and capacity hypotheses against the service objective.

An apparent outlier can reflect workload mix, queueing, runtime state or a degraded execution path. Normalize the comparison, run a matched replay and use critical-path arithmetic to test possible causes.

Verify the throughput definition and service outcomes, compare resolved configurations and execution traces, then isolate supported changes. Quantify capacity replacement correctly and choose rollback from the full release risk.

A partial drop does not rule out the parser. Define success and its denominator, trace failures through encoding, generation, parsing and execution, then replay the relevant boundary with controlled versions.

Measure exposed launch and dispatch delay, actual graph coverage, capture memory and warmup. Batch size influences the result, but does not alone determine whether graphs improve the service.

Follow a shard index to the tensors a loader actually receives. Diagnose a missing file and a shape mismatch, then check architecture, quantization and request compatibility before changing hardware.

A bandwidth regression narrows the search only after the experiment is defined. Work through rank-count normalization, controlled path tests and the difference between collective time and training progress.

Separate NCCL logging, device selection and execution overrides from PyTorch failure handling. Work through a faster collective that makes the job slower, and learn which timeout actually fired.

Investigate a firmware-correlated regression with matched metrics, mapped paths and a controlled canary. Separate a configuration hypothesis from evidence that confirms it.

Diagnose GPU pod failures by scheduling, container startup and execution boundaries, without treating Allocatable as a health certificate.

Trace the job from quota reservation through admission to physical placement. Count eligible node shapes, not just free GPUs, and use controller and scheduler evidence to choose the least disruptive fix.

Find where useful progress stopped by comparing per-rank traces, host stacks and device events. Distinguish late arrival, mismatched collectives and stalled execution before choosing a recovery action.

Find the critical-path delay using local phase timing and controlled comparisons, rather than diagnosing a node from utilization or clock ratios.

Follow a request from the client's connection pool to the first text on screen. Work the timing budget, locate a buffering delay and choose what to trace without losing the slow requests.

Check useful progress before interpreting GPU activity. Run a bounded trace exercise that exposes a rank-conditional call, equal-count ordering errors and matching shapes with the wrong tensor meaning.

Explain how a plausible diagnosis became too certain, what disproved it and what changed in your investigation. Separate observed symptoms from causal evidence.

Move a vendor case forward with controlled comparisons, a faithful reproduction and a safe mitigation. Escalate by impact while evidence improves.