Mistral AI AI Infrastructure Engineer interview questions

Every MoE layer routes each token to eight experts that live on other GPUs, twice per layer, forward and back. The bytes per rank per layer for DeepSeek-V3, why that all-to-all runs close to one-to-one against compute, and why a single hot expert stalls every rank waiting at the combine.

A router that sends 90% of tokens to 26 of 256 experts makes those ranks do nine times their share while everyone else waits. The arithmetic of the imbalance, what a capacity factor drops on the floor, why the auxiliary loss fights the model, and the bias-only balancing DeepSeek-V3 used instead.

Memory follows total parameters and compute follows active ones, so the two models pull the GPU count in opposite directions. The chain for a 235B-total, 22B-active MoE against a dense 70B, for serving and then for training.

One forward pass reads every weight. Whether that read is the bottleneck depends on how many tokens ride along with it, and the answer is different for the two halves of a request.

Every token a model has seen leaves a key and a value in every layer. Multiply that out for a 70B model and you will see why memory, not compute, caps how many users a GPU can hold.

A batch of eight requests finishes when the longest one does, and seven slots compute padding until then. The fix is to make the scheduling unit one decode step, and the throughput math shows why that was worth several times the hardware.

Before vLLM, engines reserved the maximum context for every request up front and wasted most of it. The fix borrowed virtual memory from the operating system, and the numbers show how much capacity it returned.

Tokens per second flatters a system that is failing its users. The metrics that matter are two latencies at a percentile and the fraction of requests that meet both.

Weights take a fixed slice, the KV cache takes the rest, and the rest divided by context is your user count. Work it in bf16 and fp8, then find out why the answer also depends on how you split the model.

One 20k-token prompt can freeze every active stream for most of a second. The fix slices it into per-step budgets, and the budget number is a trade between two SLOs you can compute.

A small model guesses four tokens and the big one checks them in a single step. That converts idle bandwidth into tokens, and the arithmetic tells you exactly which batch size stops it working.

Same model size, three attention layouts, a 30x spread in cache per token. The formulas are short, and they explain why DeepSeek can hold four times the context of a dense 70B on the same cards.

Weight-only int4, fp8 weights and activations, or fp8 for the cache alone: each fixes a different bottleneck. Match the method to the resource you are short of, then run the evals that catch what perplexity misses.

Prefill and decode fight over the same GPU and each ruins the other's latency. Putting them on separate pools ends the fight, at the price of shipping every request's cache across the network. The break-even is a number you can derive.

The same 2,000 tokens are prefilled a thousand times an hour. Caching their KV by content turns that into one prefill and a table lookup, and a radix tree is what makes multi-turn and branching agents share it too.

Sequential is fifty seconds; one batch is four. In between are the questions the interviewer is holding: how you pack ragged inputs, what a batch window costs, and where the memory stops you.

A flat median with a broken tail means one in a hundred requests is hitting something the others do not. There are five usual suspects, and the order you rule them out in is the answer.

Three engines, one hardware roofline, and the difference between them is which part of the roofline each one reaches first on your workload. The decision is a table, and the table has reversal conditions.

Three hundred fine-tunes as three hundred deployments would need three hundred GPUs. Batched adapters put them on one base, and the arithmetic says what each extra adapter costs in memory and in step time.