Each test is 5 questions with varying difficulty.
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vLLM is the industry-leading open-source library for high-throughput, memory-efficient LLM inference and serving. Developed at UC Berkeley, it introduced PagedAttention, a KV cache memory management algorithm inspired by OS virtual memory, that enables 2-4x higher GPU throughput compared to naive static KV cache allocation. By 2026, vLLM has become the de-facto standard for self-hosted LLM serving at production scale.
vLLM interview questions assess whether a candidate understands the internals of LLM inference optimisation, not just how to start a server. Junior engineers are expected to know the core PagedAttention benefit and basic configuration parameters (gpu_memory_utilization, max_model_len, tensor_parallel_size). Mid-level engineers must reason about continuous batching mechanics, block manager operation, and prefix caching. Senior engineers are assessed on multi-node tensor parallelism with Ray, speculative decoding configuration, and diagnosing KV cache fragmentation under production load.
Before PagedAttention, LLM serving systems reserved a contiguous block of GPU memory for each request's KV cache at request start. Since output length is unknown at request time, systems over-allocated, reserving the maximum context length for every request. With typical batch sizes, this left 60-80% of GPU memory wasted on internal fragmentation, severely limiting concurrency.
PagedAttention eliminates this waste by storing KV cache in fixed-size non-contiguous blocks (pages), allocated on demand as tokens are generated. This brings GPU memory utilisation from 20-40% to 90%+ under real-world load distributions, directly translating to 2-4x higher request throughput on the same hardware.
As an interview topic, vLLM questions reveal whether a candidate understands the memory-bandwidth-bound nature of LLM inference. Explaining why increasing batch size improves throughput but degrades tail latency, how prefix caching reduces TTFT for shared system prompts, and when speculative decoding is beneficial versus harmful demonstrates the GPU systems expertise that AI infrastructure roles require.
vLLM utilizes a custom execution engine that bypasses standard static batching. The architecture centers on the Scheduler, which manages requests, and the BlockManager, which handles physical memory allocation for the KV cache.
Requests enter the Scheduler, which determines if they can be added to the current batch. The BlockManager maps tokens to physical blocks in the KV Cache Pool. The Model Executor runs the forward pass, and the results are returned to the user.
User Request
↓
[Scheduler]
↓
[Block Manager] ↔ [KV Cache Pool]
↓
[Model Executor]
↓
[GPU Compute]
↓
ResponseImplementing non-contiguous KV cache storage by partitioning sequences into fixed-size blocks managed by a block table.
Trade-offs: Reduces memory fragmentation vs. adds complexity to the attention kernel.
Integrating new requests into the batch at the token level rather than the sequence level.
Trade-offs: Increases GPU utilization vs. requires sophisticated scheduler logic.
Storing KV cache for common prompt prefixes in a cache pool to avoid re-computation.
Trade-offs: Saves compute cycles vs. consumes additional GPU memory.
| Reliability | Configure health check endpoints (`/health`) and readiness probes (`/health/ready`) in Kubernetes deployments to detect when the model engine has finished loading. Implement graceful shutdown handling so in-flight requests complete before the pod terminates. Use multiple vLLM replicas behind a load balancer to eliminate SPOF. |
| Scalability | Scale horizontally by adding more vLLM instances behind a load balancer; use Ray for multi-node distribution. |
| Performance | Tune gpu_memory_utilization to 0.85-0.92 (leave headroom for CUDA kernels). Set max_num_seqs to match expected concurrent users. Enable prefix_caching=True for workloads with shared system prompts to reduce TTFT by 40-60%. For multi-GPU, tensor_parallel_size=N should match the number of GPUs; pipeline_parallel_size for cross-node deployments. Target p50 TTFT < 100ms, p99 < 500ms. |
| Cost | Use quantization (AWQ/FP8) to fit larger models on smaller, cheaper GPUs. |
| Security | Restrict API access via authentication proxies; sanitize inputs to prevent prompt injection. |
| Monitoring | Track vllm_request_queue_time, vllm_gpu_cache_usage, and vllm_tokens_per_second. |
vLLM implements PagedAttention and continuous batching, which significantly reduce memory fragmentation and increase throughput compared to the standard Transformers library, which often uses static batching and contiguous memory allocation.
Standard attention requires contiguous memory for the KV cache, leading to fragmentation. PagedAttention partitions the KV cache into blocks, allowing non-contiguous memory allocation, similar to virtual memory in operating systems.
No, vLLM is specifically designed for high-throughput inference and serving, not for training or fine-tuning models.
Continuous batching is a technique where the engine processes new requests as soon as others finish, rather than waiting for all sequences in a batch to complete, maximizing GPU utilization.
vLLM is memory-efficient because it uses PagedAttention to eliminate internal memory fragmentation, allowing it to use nearly all available GPU memory for the KV cache without waste.
vLLM supports most popular transformer-based architectures, but it requires specific kernel implementations for each, so it may not support niche or custom architectures immediately.
The block manager maps logical token indices to physical memory blocks in the GPU, enabling the non-contiguous storage required by PagedAttention.
vLLM uses dynamic request preemption, where it swaps the KV cache of lower-priority requests to CPU memory to free up space for higher-priority requests.
Prefix caching allows the vLLM engine to store and reuse the KV cache for common prompt prefixes, such as system prompts, across multiple requests to save compute cycles.
Yes, vLLM is highly suitable for low-latency applications due to its optimized CUDA kernels and efficient batch scheduling, though it is optimized primarily for throughput.
vLLM is primarily optimized for NVIDIA GPUs with CUDA support, as it relies on custom CUDA kernels for its performance benefits.
vLLM uses tensor parallelism to distribute model weights and computation across multiple GPUs, often orchestrated by Ray for multi-node deployments.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.