LLM inference performance problems often stem from system design rather than raw hardware. Key bottlenecks include GPU underutilization from heterogeneous request lengths and poor scheduling, memory-bandwidth constraints during the decode phase, and host-side CPU overhead from tokenization and prompt preprocessing. The post explains the prefill vs. decode distinction, why static batching falls short, and how techniques like continuous batching, paged/block-based KV-cache management, prefix caching, and chunked prefill address these issues. A comparison table contrasts traditional vs. modern serving designs (vLLM, TGI, TensorRT-LLM), and a practical recommendations table maps specific problems to actionable fixes across metrics, architecture, batching policy, and resource separation.
Table of contents
Key TakeawaysHidden Bottleneck #1 — GPU UnderutilizationHidden Bottleneck #2 — Memory Bandwidth, Not Raw ComputeHidden Bottleneck #3 — Latency vs Throughput TradeoffsHidden Bottleneck #4 — Batching StrategyHidden Bottleneck #5 — KV Cache Waste and ReuseHidden Bottleneck #6 — Tokenization and CPU-Side OverheadvLLM vs Traditional Serving ArchitecturesHow to Fix These Bottlenecks in PracticeFAQ SECTIONConclusionReferencesSort: