Groq AI Infrastructure Engineer interview questions

Trace a two-by-two output-stationary array cycle by cycle. Derive its fill and drain cost without turning a toy schedule into a TPU benchmark.

Follow a dependent read path to understand SRAM locality, aggregate bandwidth and the difference between one fast stream and total serving throughput.

Define what a throughput claim counts, then compare its required arithmetic and memory traffic with a feasible deployment and matched latency targets.

Trace a grid into blocks and warps, then distinguish resident work from ready work. Derive an occupancy limit without treating it as a speed target.

Follow one request from API admission to streamed output, then zoom into the token-generation loop. See where the cache lives, who schedules work, and how workload measurements turn the drawing into a capacity plan.

Follow the weight bytes through a small decode batch and a long prefill. A scoped roofline calculation explains the difference without pretending to predict actual latency.

Count two matrix shapes to derive the H100 ridge, throughput ceilings and time floors. Compare an illustrative measurement with its bound before deciding what to optimize.

Trace GPU credits through reservation, allocation, metering and settlement, with crash-safe ownership and a separate fairness policy.

Separate storage scope from physical backing, then follow a warp through adjacent and strided loads. Count useful bytes and requested sectors before inferring HBM traffic or speed.

Derive the weight-only shortcut, then add KV traffic and feasible eight-GPU placement. A worked latency floor shows why batch size alone cannot establish the decode bottleneck.

Normalize link directions, count collective bytes, and see why a gradient reduction can outlast its real overlap window.

Follow a token into retained attention state, derive bytes per position for a 70B model, and distinguish present cache usage from the room needed to finish a response.

Track request admission through a concrete page-reservation example, then connect scheduling, growth and stream cleanup to measured service limits.

Derive a 10,240-GPU layout from model work, then account for collective traffic, durable checkpoints, power and replacements. A nested diagram connects tensor, pipeline and data parallel groups to the fleet.

Work from each GPU's cache budget, reserve room for generated tokens, then check latency separately. A limiting-rank example shows why fitting today's prompts is not enough to admit tomorrow's work.

Compare useful parallel work, data movement and reuse. A synthetic CPU/GPU crossover shows why a faster kernel can still lose the end-to-end request.

Work from token timestamps to first-token wait, average pace and individual stalls. Then count requests meeting both latency limits, keeping failures and measurement boundaries explicit.

Draw query-head sharing, count actual stored state and compare payloads with explicit model geometry and binary units.

Use a flexible 45-minute plan to connect requirements, a request path, capacity arithmetic, selected deep dives and a clear closing decision.