I started with the API gateway and worked toward the GPU pool, covering auth, quota enforcement, and a request queue in between.
Start by clarifying requirements and scale, then walk through the request path layer by layer—API gateway, load balancing, batching/scheduling, model workers, and response streaming—highlighting key design decisions and trade-offs at each stage. Emphasize how you would handle latency, throughput, and cost constraints while ensuring reliability and scalability.
Pro tip: Focus on the unique challenges of LLM inference: variable request sizes, long generation times, and GPU memory constraints. Show you understand that batching and scheduling are critical for efficiency, and that streaming responses improve perceived latency.
Ask about expected QPS, model sizes, latency SLOs, and cost constraints to frame the design. Establish whether the system is for real-time interactive use or batch processing.
Describe how requests enter the system: authentication, rate limiting, routing to appropriate model versions, and load balancing across workers. Mention the need for sticky sessions or consistent hashing for stateful models.
Explain how requests are queued and dynamically batched to maximize GPU utilization. Discuss trade-offs between batch size, latency, and throughput, and how to handle timeouts and prioritization.
Detail the model serving infrastructure: GPU workers, model loading, memory management, and autoscaling. Cover techniques like model parallelism, quantization, and caching to optimize performance.
Describe how to stream tokens back to the client, manage partial failures, and ensure reliability with retries and fallbacks. Discuss monitoring and observability for performance tuning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Continuous batching was the answer they were clearly fishing for.
Start by framing the core tension between maximizing GPU utilization through larger batches and meeting strict per-request latency targets like TTFT and TPOT. Then describe a continuous batching system with dynamic batch sizing, prioritization, and preemption, and explain how you would tune and monitor it to balance throughput and latency.
Pro tip: Emphasize that latency targets are per-request SLOs, not just averages—design for tail latency by using techniques like chunked prefill and selective batching, and always validate with real traffic patterns.
Ask about the specific latency targets (e.g., p99 TTFT < 200ms, TPOT < 50ms), traffic patterns, and hardware. This shows you understand that batching strategies depend on concrete SLOs and workload characteristics.
Propose continuous batching (iteration-level scheduling) over static batching, as it allows new requests to join mid-flight and finished requests to leave, keeping the GPU busy without waiting for the slowest request.
Describe how to dynamically adjust batch size based on current load and latency headroom, using a scheduler that prioritizes requests to meet TTFT and TPOT targets, potentially with preemption for high-priority requests.
Mention techniques like chunked prefill to interleave prefill and decode phases, selective batching for operations with different compute characteristics, and memory management (e.g., PagedAttention) to reduce fragmentation and allow larger batches.
Explain how you would instrument the system to track GPU utilization, TTFT, TPOT, and tail latencies, then use that data to tune batch size limits, scheduling policies, and preemption thresholds to meet SLOs under varying load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed KV-cache reuse, prefix caching, speculative decoding, and quantization.
Start by framing latency as a multi-layered problem across the serving stack, then walk through optimizations at each layer (model, system, and network) while highlighting trade-offs. Emphasize that the best approach depends on workload characteristics and SLAs, and mention how you would measure and iterate.
Pro tip: Quantify the impact of each technique with metrics like TTFT and TPOT, and discuss how you'd balance latency against throughput and cost—showing you understand real-world constraints at OpenAI's scale.
Clarify what latency means in this context (e.g., time-to-first-token, time-per-output-token) and establish target SLAs based on use case. This sets the foundation for prioritizing optimizations.
Discuss model architecture and inference optimizations such as quantization, pruning, distillation, and kernel fusion. Mention techniques like speculative decoding and KV cache management.
Cover batching strategies (continuous batching, dynamic batching), scheduling, and hardware acceleration (GPU/TPU utilization, tensor parallelism). Include memory management and caching.
Address network latency with edge deployment, CDNs, and efficient protocols (gRPC, HTTP/2). Discuss load balancing, autoscaling, and geo-distribution.
Explain how you would instrument the stack, run A/B tests, and balance latency against throughput, cost, and accuracy. Highlight the importance of continuous profiling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through autoscaling based on queue depth and GPU utilization metrics, then got into spot instance interruption handling.
Start by clarifying the workload characteristics and SLOs, then propose a multi-tier GPU pool with autoscaling, mixing on-demand for baseline and spot for burst, and a routing layer that directs requests to the most cost-effective model variant. Emphasize trade-offs between cost, latency, and reliability, and how you'd monitor and adjust dynamically.
Pro tip: Quantify the cost savings from spot instances and model routing (e.g., 'spot can cut costs 60-70% for fault-tolerant workloads') and mention how you'd handle spot interruptions gracefully with checkpointing and fallback to on-demand.
Ask about workload types (training vs inference), latency SLOs, fault tolerance, and budget. This determines the mix of instance types and routing policies.
Propose a pool with on-demand instances for baseline capacity and spot instances for elastic burst. Use autoscaling groups and consider reserved instances for steady-state.
Route requests across multiple models (e.g., different sizes or versions) based on cost, latency, and accuracy requirements. Use a load balancer with health checks and fallback logic.
Design for graceful degradation: checkpoint long-running jobs, drain spot instances, and automatically shift traffic to on-demand or other regions.
Set up cost and performance dashboards, track spot savings and interruption rates, and continuously tune the routing and scaling policies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Load shedding and circuit breakers I covered fine.
Structure your answer around the three concerns—overload, cascading failures, and multi-region availability—and for each, describe detection, mitigation, and trade-offs. Emphasize that LLM serving has unique constraints like long-running requests and GPU scarcity, so reliability strategies must adapt accordingly. Conclude by discussing how you'd validate these mechanisms through chaos testing and monitoring.
Pro tip: Frame reliability as a product requirement: tie each mechanism to user-facing SLOs (e.g., p99 latency, error rate) and explain how you'd measure and alert on them. This shows you think beyond infrastructure to customer impact.
Ask about expected traffic patterns, latency SLOs, cost constraints, and whether the system is multi-tenant. This ensures your answer is tailored to the actual scale and priorities.
Discuss admission control (e.g., token bucket, concurrency limits), request prioritization, and graceful degradation (e.g., shorter max tokens, smaller models). Mention autoscaling with GPU-aware metrics.
Explain circuit breakers, timeouts, retries with jitter, and bulkheads to isolate failures. Highlight the need for backpressure and load shedding to avoid retry storms.
Describe active-active or active-passive setups, data replication for model weights and caches, and global load balancing with health checks. Discuss failover strategies and consistency trade-offs.
Mention chaos engineering, load testing, and observability (metrics, tracing, logging) to continuously validate reliability. Emphasize learning from incidents and refining mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's critical user journeys and failure modes, then propose a layered observability stack (metrics, logs, traces, and product analytics) tailored to those needs. Explicitly discuss cost tradeoffs by comparing sampling, aggregation, retention, and tooling choices, and tie them back to business impact and SLOs.
Pro tip: Emphasize that observability should be driven by SLOs and error budgets, not by collecting everything—this shows you understand cost discipline and prioritization. Also, mention that you'd start with high-level metrics and progressively add granularity only when debugging requires it.
Ask questions to understand the system's architecture, critical user journeys, and business objectives. Identify key SLOs and potential failure points to focus observability efforts.
Outline the four pillars: metrics (for alerting and dashboards), logs (for detailed debugging), traces (for request flow and latency), and product analytics (for user behavior). Explain how each layer addresses different needs.
Compare costs of different approaches: e.g., high-cardinality metrics vs. logs, sampling rates for traces, retention periods, and managed vs. self-hosted solutions. Highlight the tradeoff between granularity and cost.
Explain how you'd start with essential metrics and alerts, then add more detailed observability as needed. Emphasize using SLOs to decide what to monitor and when to invest more.
Connect observability choices to business outcomes: faster incident resolution, better user experience, and cost efficiency. Show how you'd measure the ROI of observability investments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.