LLM inference serving systems are the infrastructure stack that makes large language models available in production. These systems handle model loading, request batching, KV-cache management, speculative decoding, and continuous batching to maximize throughput while minimizing latency for end users.
Key Characteristics
Continuous Batching: Dynamically add and remove sequences from batches as they complete, maximizing GPU utilization
KV-Cache Management: Efficient memory management for the key-value cache across concurrent requests
Speculative Decoding: Use a smaller draft model to propose tokens that the large model verifies in parallel
Quantization: Reduce model precision (FP16 to INT4/INT8) for faster inference with minimal quality loss
Popular Models
vLLM: High-throughput serving engine with PagedAttention for efficient KV-cache management
TensorRT-LLM: NVIDIA's optimized inference framework with graph compilation and kernel fusion
TGI (Text Generation Inference): HuggingFace's production-grade inference server
llama.cpp: CPU-first inference engine with GPU acceleration, focused on local deployment
Build This Pattern
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Explain the serving-layer architecture behind production LLM inference systems. Architecture: describe the four core optimization techniques that form the foundation of modern LLM serving. First, continuous batching: unlike static batching where a batch waits for its slowest sequence, continuous batching swaps in new requests at every token step as sequences finish, keeping GPU utilization near 100% even with heterogeneous request shapes — this alone delivers 2-5x throughput improvement. Second, PagedAttention (vLLM's core innovation): inspired by OS virtual memory, it stores each sequence's KV-cache in non-contiguous fixed-size blocks (typically 16 tokens per block per layer) with a per-sequence block table mapping logical positions to physical blocks, eliminating internal fragmentation and raising memory utilization from ~35% to ~96%. Third, prefix caching: when many requests share a common system prompt prefix, compute the KV-cache for that prefix once and reuse it across all requests via copy-on-write block sharing — this yields 30-90% latency reduction on time-to-first-token for agent workloads and templated prompts. Fourth, speculative decoding: a small draft model speculates multiple tokens ahead, the large target model verifies them in a single forward pass, and accepted tokens are committed — achieving 1.5-3x speedup when the draft model has high acceptance rates. Cover how these techniques compose in production engines like vLLM, TensorRT-LLM, SGLang, and TGI. Discuss supporting techniques: chunked prefill (breaking long prompts into chunks to avoid starving decode requests), tensor parallelism (splitting model weights across GPUs), FlashAttention (reducing attention memory from O(n^2) to O(n) via IO-aware tiling), and KV-cache quantization (INT8/FP8 compression with calibration). Error handling: discuss memory fragmentation when serving variable-length sequences under high concurrency, preemption strategies when GPU memory is exhausted (swap KV-cache to CPU or recompute), handling request timeouts in continuous batching where long generations block slot recycling, and the challenge of maintaining consistent latency under bursty traffic patterns. Edge cases: behavior at batch size 1 where PagedAttention memory wins are minimal and hand-tuned kernels often beat vLLM, serving models with grouped-query attention (GQA) or multi-query attention (MQA) where KV-cache size varies dramatically by architecture, handling multi-modal inputs (images, audio) that have different tokenization costs per request, and cold-start latency when loading large models into GPU memory. Best practices: include practical deployment guidance — small services use vLLM + AWQ 4-bit + prefix caching, large services use TensorRT-LLM or vLLM + tensor parallelism, lowest-latency paths use speculative decoding + CUDA graphs. Discuss monitoring: track time-to-first-token (TTFT), time-between-tokens (TBT), throughput (tokens/sec/GPU), and KV-cache utilization (target 90%+). Cover GPU procurement considerations as inference workloads are projected to consume 2/3 of all AI compute by 2026. Testing: suggest benchmarking throughput at various concurrency levels (1, 10, 50, 100 requests), measuring P50/P95/P99 latency tails, stress-testing memory limits with long-context requests, validating prefix cache hit rates under realistic traffic patterns, and comparing speculative decoding acceptance rates across model pairs.