Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI Researcher

LLM inference requires balancing memory, latency, and throughput to maintain cost-effective, high-quality production services. The primary bottlenecks—memory consumption, time-to-first-token (TTFT), and inter-token latency—stem from the compute-intensive nature of transformer layers and the sequential generation of tokens. Optimizing these processes involves model-level techniques like post-training quantization and efficient attention mechanisms such as Grouped Query Attention (GQA) and Multi-head Latent Attention (MLA), which reduce memory footprints. On the serving side, strategies like PagedAttention, continuous batching, and prefix caching significantly improve throughput by reducing memory fragmentation and recomputation. While VLLM serves as a robust production default for standard workloads, SGLang offers superior performance for complex agentic workflows. Ultimately, selecting the right inference engine and optimization strategy depends on specific latency service-level objectives and the nature of the application's token usage.