1. End-to-End LLM Serving Pipeline Design: Suppose you must design a production inference service for a 70B-parameter language model that handles hundreds of requests per second while keeping tail latency low (e.g. 95th percentile under 500ms for a moderate response length). How would you architect this system from the ground up? Consider the model deployment across hardware (GPU distribution), how you’d batch or queue requests, and what optimizations you would employ to achieve high throughput and low latency. (Tests the candidate’s ability to holistically design a large-scale LLM inference system under strict latency and throughput constraints.)
2. Dynamic Batching Strategies: LLM inference is iterative and can benefit greatly from batching multiple requests together. How would you implement a dynamic batching mechanism for incoming queries to maximize GPU utilization without introducing unacceptable latency for individual requests? Discuss how you’d handle varying input lengths and generation lengths (to avoid long prompts slowing down shorter ones), and compare approaches like fixed-size batches vs. continuous batching with fine-grained scheduling. (Tests understanding of request batching policies and how to balance throughput vs. per-request latency, e.g. via smart scheduling and grouping of requests.)
3. Key-Value Cache Utilization: In autoregressive generation, the model can cache the key/value pairs from prior tokens to avoid recomputing them on each step. How could you leverage KV caching to speed up inference in a multi-turn conversation or for repeated prompts across requests? Describe how you might implement a cache for previously computed states and discuss the memory vs. compute trade-offs involved. How would you decide when to reuse or discard cached states, and what are the challenges in managing cache consistency in a high-throughput setting? (Tests knowledge of transformer KV caching mechanics and the ability to weigh memory overhead against compute savings in practice.)
4. Model Quantization and Precision Trade-offs: If GPU memory and throughput are at a premium, one option is to compress the model. Explain how you would use quantization (e.g. 8-bit or 4-bit weights) to reduce the model’s memory footprint and possibly increase inference speed. What are the impacts of lower precision on model accuracy and on hardware performance (throughput/latency)? Additionally, discuss any implementation considerations—such as quantization-aware training vs. post-training quantization or runtime decomposition techniques—and how those might affect a production inference pipeline. (Tests understanding of model compression techniques and their real-world effects on performance and accuracy in an inference setting.)
5. Parallelism and Model Sharding: When a single GPU isn’t sufficient to host or compute the model, how would you split a large LLM across multiple GPUs or machines? Compare tensor/model parallelism (splitting individual layers across GPUs) with pipeline parallelism (dividing the stack of layers among GPUs in sequence) for inference. How does each approach affect latency and throughput? Discuss the challenges you’d need to address (such as synchronizing between devices, communication overhead, and load balancing) to make multi-GPU inference efficient and reliable. (Tests knowledge of distributing a model over multiple devices and the trade-offs between different parallelization strategies in terms of performance and complexity.)
6. Speculative Decoding for Faster Generation: Describe what speculative decoding is and how it can be used to accelerate LLM inference. In what scenario would you employ a speculative decoding approach, and how does it leverage a smaller “draft” model alongside the large model to reduce end-to-end latency? Explain the potential speed-ups and also the complexities or downsides of this technique (for example, managing two models, ensuring consistency of the final output, or wasted computation when the speculation is incorrect). (Tests understanding of an advanced inference optimization technique and the ability to reason about its benefits and implementation challenges.)
7. Memory Offloading and Management: Imagine your model and its intermediate data (like activation maps or the KV cache) don’t all fit in GPU memory during inference, especially with long context inputs. How would you design an offloading policy to move parts of the model or data to CPU memory (or even NVMe storage) and bring them back when needed? Discuss what factors you’d consider in an offloading strategy – for instance, which layers or data to offload, how to overlap data transfer with computation to hide latency, and how PCIe or interconnect bandwidth constraints come into play. What are the performance trade-offs of offloading, and how can smart scheduling minimize the impact on latency? (Tests the candidate’s grasp of memory–compute trade-offs and ability to manage limited GPU memory by trading off transfer overhead, as seen in large-model inference scenarios.)
8. Throughput vs. Latency Trade-offs: In a high-volume LLM service, you often need to maximize total throughput (tokens/sec or queries/sec) while still meeting latency requirements for individual users. How would you balance this trade-off in practice? Consider ideas like using adaptive batch sizes (batching more aggressively during peak load vs. prioritizing low latency for realtime requests), deploying separate model replicas or service tiers for high-priority low-latency requests vs. lower-priority bulk requests, or any scheduling/allocation mechanism to ensure both objectives are met. Discuss how you would evaluate the latency–throughput sweet spot and adjust the system as load patterns change. (Tests understanding of operational trade-offs in system design and the ability to devise strategies that cater to different service level objectives for throughput and latency.)
9. Fault Tolerance in Inference Pipelines: Serving large models is not only about speed – it’s also about reliability. Suppose a generation request is part-way through when a GPU server fails or a network hiccup occurs. How could you design the system to be fault-tolerant in such cases? Discuss mechanisms like checkpointing or saving intermediate state so another node could resume if possible, retrying requests from scratch (and what that means for user experience), or running duplicate inference in parallel on redundant hardware to hedge against failures. What are the pros and cons (especially in cost and complexity) of these approaches in a production, high-throughput inference environment? (Tests the candidate’s ability to incorporate reliability and failure-handling into system design, recognizing the challenges of long-running sequential processes like LLM inference.)
10. Cost-Efficiency and Scalability Considerations: Large-scale LLM inference can be extremely expensive. What strategies would you use to optimize cost while maintaining acceptable performance? Discuss options such as using smaller or distilled models for certain tasks or routing simpler queries to cheaper models, leveraging spot instances or scale-to-zero for unused capacity, sharing GPUs across multiple models or clients (multi-tenancy) to increase utilization, and using techniques like batch processing or quantization to reduce resource usage. How would you ensure the system scales cost-effectively with demand, and what trade-offs might you have to accept to stay within budget? (Tests the candidate’s ability to think beyond pure performance and design a solution that is economically sustainable, demonstrating awareness of real-world constraints like resource cost and utilization.)