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Start by clarifying requirements and SLOs, then sketch a high-level architecture with a request router, batching queue, and GPU worker pool. Dive into the batching strategy and scheduling policies, explaining how they balance throughput and latency while supporting multiple models and versions. Conclude with trade-offs and failure handling.
Pro tip: Emphasize that batching must be compatible (same model, version, and input shape) and discuss dynamic batching with a timeout to avoid latency violations. Show awareness of GPU memory constraints and model swapping costs.
Ask about expected QPS, model types, latency SLOs (e.g., p99), and hardware constraints. Define what 'compatible' means for batching.
Propose components: API gateway, request queue, batching scheduler, GPU worker pool, model registry, and monitoring. Explain how requests flow through the system.
Describe dynamic batching with a max batch size and timeout, and a scheduling policy that prioritizes latency-sensitive requests while maximizing GPU utilization.
Explain how to load multiple models/versions on GPUs, possibly using model multiplexing or separate worker pools, and how to route requests to the correct version.
Discuss trade-offs between throughput and latency, and strategies for handling GPU failures, overload, and model updates without downtime.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed model ID, model version, input dtype, and sequence length range.
Start by defining what 'compatible' means for batching inference requests: they must share the same model, tokenizer, and execution configuration, and their input/output shapes must be compatible for concatenation. Then distinguish between mismatches that only affect efficiency (e.g., different sequence lengths causing padding overhead) and those that break correctness (e.g., different models or tokenizers leading to wrong outputs).
Pro tip: Emphasize that correctness bugs arise when batching changes the semantics of individual requests, such as mixing requests with different sampling parameters (e.g., temperature, top-p) or different stop sequences, because the batch-level implementation may apply one set of parameters to all. This shows you understand the subtle pitfalls beyond obvious model mismatches.
List the dimensions along which requests must match for safe batching: model architecture and weights, tokenizer, input/output tensor shapes, and execution configuration (e.g., dtype, device).
Explain that some mismatches (e.g., varying sequence lengths) only cause inefficiency due to padding, while others (e.g., different models) produce incorrect results if batched.
Pinpoint the mismatch that is a correctness bug: batching requests that require different sampling parameters (e.g., temperature, top-k, top-p) or different stop conditions, because the batch will apply a single set of parameters, altering outputs.
Mention how to handle incompatible requests: group by compatibility keys, use separate batches, or implement per-request parameter handling if the system supports it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I gave three triggers: batch size cap hit, linger timeout, and worker becoming available with an idle queue.
Start by enumerating the conditions that should trigger a dispatch: batch size threshold, linger time limit, queue drain, and system signals like memory pressure. Then explain how to derive the linger time from the latency SLO by subtracting expected processing and network overhead, and validating with tail latency measurements.
Pro tip: Mention that the linger time should be set as a fraction of the SLO budget to leave room for other latency contributors, and that you'd monitor the actual end-to-end latency distribution to adjust it dynamically.
List the conditions that cause the scheduler to stop waiting: batch size reached, linger time expired, queue empty, or external signals like shutdown or memory pressure.
Break down the end-to-end latency SLO into components: queue wait, batch formation (linger), processing, and network. Allocate a portion to linger.
Set linger time = SLO budget for batching minus expected processing and network overhead, ensuring it's a fraction of the total to allow for variability.
Measure actual latency percentiles (p50, p95, p99) and adjust linger time to meet SLO without excessive batching delay.
Discuss how linger time affects throughput and latency; longer linger improves batching efficiency but risks SLO violations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was a follow-up and it's where I felt most out of my depth.
Start by contrasting fixed-cost models (e.g., BERT) where each request has a known, static compute cost, with autoregressive LLMs where generation is iterative and per-token cost varies. Explain how the scheduler must shift from batch-level scheduling to token-level scheduling with continuous batching, and identify the new binding constraint (often KV cache memory or memory bandwidth) rather than compute.
Pro tip: Emphasize that the binding constraint shifts from compute to memory bandwidth and KV cache capacity, and that scheduling decisions must now consider memory fragmentation and eviction policies to maintain high utilization.
Describe how fixed-cost models have uniform, predictable execution times per request, while autoregressive LLMs generate tokens sequentially with variable output lengths, leading to dynamic and heterogeneous workloads.
Detail why static batching is inefficient for LLMs: it wastes compute on padding and cannot adapt to early-finishing sequences. Introduce continuous batching (iteration-level scheduling) where new requests can join and finished ones leave at each token step.
Argue that with continuous batching, compute is no longer the bottleneck; instead, KV cache memory capacity and memory bandwidth become the limiting factors because each active sequence requires storing keys and values for all previous tokens.
Mention techniques like PagedAttention to manage KV cache memory efficiently, and how scheduling must balance batch size, memory usage, and latency to maximize throughput without causing out-of-memory errors.
Summarize that the scheduler must be memory-aware and dynamic, and that the binding constraint is the KV cache size (or memory bandwidth), which dictates the maximum number of concurrent sequences and overall system throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workload and metrics (e.g., batch size, sequence length, model size, GPU type) to rule out measurement artifacts. Then hypothesize that the bottleneck is not raw compute but memory bandwidth, data loading, or synchronization overhead, and propose a systematic tuning plan that addresses each layer of the stack.
Pro tip: Emphasize that low GPU utilization with rising p99 often indicates tail latency from stragglers or queuing effects, not just a slow kernel. Mention that you'd check for GPU memory fragmentation and CUDA context switching as subtle culprits.
Ask about the model, batch size, sequence length, GPU type, and how utilization and latency are measured. Confirm whether p99 is end-to-end or per-inference, and whether the workload is steady or bursty.
Check if the GPU is idle waiting for data (CPU preprocessing, I/O, network), or if kernels are inefficient (small ops, poor occupancy). Use profiling tools like Nsight Systems or PyTorch Profiler to see gaps and kernel durations.
Investigate stragglers, queuing delays, memory fragmentation, or thermal throttling. Look at per-request latency distribution and GPU memory usage over time to spot spikes or leaks.
Suggest concrete fixes: increase batch size or use dynamic batching, optimize data pipeline with prefetching and pinned memory, enable CUDA graphs, use mixed precision, or adjust kernel launch parameters.
Recommend A/B testing changes with controlled experiments, monitoring both utilization and p99 latency. Emphasize that tuning is iterative and should be guided by profiling data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-tenant quota on queue slots per bucket was my answer.
Start by acknowledging the problem: a single tenant's burst can monopolize a length bucket, starving others. Then describe a multi-layered defense: per-tenant quotas, fair queuing, and dynamic resource allocation to ensure isolation and fairness.
Pro tip: Emphasize that fairness must be enforced at multiple levels (admission, scheduling, and preemption) and that metrics like per-tenant latency and throughput are crucial for detecting and mitigating starvation.
Explain that the length bucket is a shared resource and a burst from one tenant can exhaust its capacity, causing head-of-line blocking for others.
Describe how each tenant has a maximum number of concurrent requests or a rate limit per bucket, preventing any single tenant from consuming all resources.
Use a scheduling algorithm like weighted fair queuing or deficit round-robin to allocate bucket capacity proportionally among active tenants, ensuring no tenant is starved.
If a tenant is idle, allow others to borrow its share temporarily, but reclaim it when the tenant becomes active again, maintaining fairness over time.
Continuously monitor per-tenant usage and latency; if starvation is detected, preempt or throttle the offending tenant's requests to restore fairness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Inference is read-only so retrying is safe, no external state gets corrupted.
Start by clarifying the system architecture and failure model, then systematically identify affected requests based on batch composition and state. Explain the retry strategy with idempotency and safety guarantees, and justify why retries are safe in this context.
Pro tip: Emphasize that retries are safe only if operations are idempotent and side effects are managed; mention that you'd log and monitor retries to detect systemic issues, showing operational maturity.
Ask questions to understand the GPU worker's role, batch processing model, and what 'crash' means (e.g., hardware failure, OOM, software bug).
Determine which requests were in the batch at the time of crash, considering in-flight vs. completed requests and any partial state.
Decide what to retry: likely the entire batch or only unacknowledged requests, depending on idempotency and checkpointing.
Justify why retrying is safe: idempotent operations, no side effects, or deduplication mechanisms prevent duplicate processing.
Mention logging retries, alerting on frequent crashes, and potential improvements like checkpointing or smaller batches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.