← Coding for Infra / 15

hardNewBasetenAnyscale

Implement the scheduler loop of a continuous-batching engine: admit, step, preempt. Where does it livelock?

Admission, a step that grows every sequence, and preemption under memory pressure. The loop is short and it has a failure that only appears when the cache is tight: in a measured run it preempted seventeen times and completed nothing, because every preempted sequence restarted from zero.

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.

Admission, a step that grows every sequence, and preemption under memory pressure. The loop is short and it has a failure that only appears when the cache is tight: in a measured run it preempted seventeen times and completed nothing, because every preempted sequence restarted from zero.

more free answers with an account · no card

The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

🚀 Inference & ServingSign in

Continuous BatchingContinuous batching schedules at the granularity of a single decode step instead of a whole request, so a finished sequence's slot is refilled on the next iteration rather than when the longest request in the batch ends. It is the scheduling idea that turned LLM serving from a padded, half-idle GPU into one that stays full, and it decides how the engine's scheduler, memory manager and latency SLOs interact.

🚀 Inference & Serving🔒 Premium

Chunked PrefillA long prompt's prefill can occupy a GPU for hundreds of milliseconds, and every sequence mid-decode on that GPU waits for it. Chunked prefill splits the prompt into fixed token budgets and interleaves each chunk with a decode step, so decode latency stays flat at the cost of a slower first token for the long prompt. The chunk budget is a knob between TTFT and TPOT, and the interview question is how you would set it.

🗂️ Scheduling & Orchestration🔒 Premium

Multi-Tenancy, Quotas and Fair ShareA shared GPU pool is cheaper than ten private ones because ten teams' demand is smoother than one team's, and it only works if the sharing is enforced. Quotas say what each team is guaranteed, borrowing lets idle guarantees be used by others, fair share decides who waits when everyone wants more, and preemption reclaims borrowed capacity. This page works the arithmetic that makes pooling worth it, the layers of isolation a tenant needs, and the incentive problems (hoarding, gaming, the research-versus-product tension) that any policy has to survive.

📐 AI Systems DesignSign in

GPU Job Scheduler DesignDesign a scheduler for a shared GPU cluster is the most common design prompt in AI infrastructure interviews, because it touches everything: queues and priorities, gang placement, topology, fairness across teams, preemption and the checkpoints that make it survivable, and the failure handling that keeps a 512-GPU job alive. This page builds the design in layers, states the data model and the scheduling loop, derives the numbers (how long a job waits, how much preemption costs, how much fragmentation wastes), and lists the trade-offs the interviewer will push on.

Explore the full curriculum →

UP NEXT ON YOUR JOURNEY

Next in this trackIn Go, download many model shards concurrently with a concurrency limit, a timeout and clean cancellation. Write it.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?

FEDITOR'S NOTE

Scored on the admit-step-preempt loop with a watermark, on the accounting invariant, and on recognising the preemption livelock and its two fixes.

DISCUSSION · 0

No comments yet — be the first to share your approach.