Training loss went flat at step 40k after descending normally. Walk me through how you debug it.

Distinguish stale telemetry, skipped updates and a real learning plateau using counters, parameter changes and a fixed evaluation batch.

Updated Oct 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Distinguish stale telemetry, skipped updates and a real learning plateau using counters, parameter changes and a fixed evaluation batch.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

GPUs and time-to-train calculator

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Advanced

🩺 Fleet Reliability & Observability🔒 Premium

Stragglers and HangsTrace how a late worker reaches the critical path, distinguish longer steps from lower throughput, and separate ordinary jitter from a stalled job. Worked examples show when delay is hidden, how shared jitter changes the math, and what evidence supports a repair.

Advanced

⚡ Kernels & Compilers🔒 Premium

Kernel Correctness ContractsA fast GPU kernel is useful only for inputs it handles correctly. Define shapes, strides, dtypes, numerical tolerance, storage ownership and synchronization before timing a replacement.

Advanced

📐 AI Systems Design🔒 Premium

Evaluation and Data Pipeline InfrastructureData pipelines prepare traceable training inputs; evaluation pipelines compare model behavior. Both need versioned inputs, reproducible execution and useful failure reporting.

Foundational

🧮 Open Weights & Serving Engines

Weight Formats: FP8 Blocks, MXFP4 and AWQDerive the storage cost of low-precision tensors, including scales, zero points and higher-precision exceptions. Distinguish formats from quantization methods, understand fused dequantization, and test whether a smaller checkpoint actually improves serving capacity without unacceptable quality loss.

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UP NEXT ON YOUR JOURNEY

Next in this trackAt 16,000 GPUs something fails every few hours. How do you choose the checkpoint interval, and what does the write have to look like?Next in this trackDesign a training system that survives losing a node without a human in the loop. What does elasticity cost you?Next in this trackA 16,384-GPU training job’s steps grow from 6.0 to 6.9 seconds. How would you isolate and fix the slowdown?

FEDITOR'S NOTE

Distinguish stale metrics, unchanged weights and unchanged evaluation quality. Use completed-update counts, parameter deltas and correctly weighted loss before changing the recipe.

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