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In Go, download many model shards concurrently with a concurrency limit, a timeout and clean cancellation. Write it.

Unbounded goroutines are the default failure: ten thousand shards means ten thousand concurrent connections and an immediate rate limit. The semaphore pattern, the context that makes cancellation propagate, and the two mistakes that produce a leak nothing reports.

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.

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The concepts behind this question

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Concurrency in Python, Go and C++Infrastructure code is concurrent by nature: a loader feeding a GPU, a gateway holding ten thousand streams, a controller reconciling a fleet. The coding screen tests whether you know which primitive fits which problem in the language you claim, and the three languages the field uses answer differently: Python has one interpreter lock and an event loop, Go has cheap goroutines and channels, C++ has threads, mutexes and atomics with no safety net. This page gives the model of each, works the favourite problems (a thread-safe LRU, a worker pool, a bounded fan-out) in each, and derives when threads, processes or async buy throughput.

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Producer-Consumer PipelinesA data loader, a log shipper, a batch inference job and a checkpoint writer are the same program: stages connected by bounded buffers, each running at its own pace, the slowest setting the throughput and the buffers absorbing the jitter between them. The coding screen asks you to build one (read, decode, batch, feed a consumer) and then pushes on the production questions: buffer sizes, clean stops, failure propagation, and why it runs at a third of the expected speed. This page derives throughput from stage times, implements the pipeline in threads and asyncio, and works the stop and failure semantics.

Foundational

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The GPU Credit Scheduler PatternThe most widely reported coding problem in AI infrastructure loops is a small scheduler: accounts hold credits, jobs arrive with a cost and a priority, and you must decide which jobs run, in what order, without letting any account overspend, then extend it under follow-ups (refunds, reservations, concurrency limits, fairness). It is not a trick question; it is a test of whether you can model state cleanly, pick the right data structures, keep invariants under mutation, and talk about complexity while typing. This page works the problem from the first line to the fourth follow-up, with the code, the invariants, and the derivations.

Core

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Rate-Limiting AlgorithmsA rate limiter answers one question, 'may this request proceed now?', and the three classic algorithms answer it with different shapes of fairness and memory: the token bucket allows bursts up to a capacity and refills at a rate, the leaky bucket smooths output to a fixed rate, and sliding windows count recent requests exactly or approximately. AI platforms limit in tokens as well as requests, per tenant, across many gateways, which adds two twists: a request's cost is unknown until it finishes, and the counters must be shared. This page derives each algorithm, implements the token bucket correctly, and covers both twists.

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Next in this trackRoute requests to replicas by prefix using a consistent hash ring. Why virtual nodes, and how many?Next in this trackImplement sampling from a model's logits with temperature, top-k and top-p. What are the numerical traps?Next in this trackCompute per-tenant GPU-hours for a billing period from a log of allocation start and stop events.

FEDITOR'S NOTE

Scored on a buffered channel as the semaphore, on context propagation for cancellation and timeouts, on the loop-variable capture and the goroutine leak, and on returning the first error while letting the rest finish.

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