Serving LLMs at Scale: Inference Architecture & Cost Optimization
Training gets the headlines, but inference is where the bill arrives — every request, every day. Serving large language models affordably and quickly is an architecture discipline of its own. These are the levers that matter most.
1. Continuous Batching
GPUs are throughput machines; serving one request at a time wastes them. Continuous (in-flight) batching interleaves many requests through the model together, dramatically raising utilization and tokens-per-second. It is the single biggest win for a busy endpoint.
2. The KV Cache Is Everything
Attention state (the KV cache) dominates memory during generation. Paged attention and cache reuse let you fit more concurrent sequences and skip recomputation for shared prefixes — a huge saving for system prompts and multi-turn chats. Manage cache memory deliberately; it, not FLOPs, is usually the bottleneck.
3. Smaller, Faster Models
Quantization (8-bit, 4-bit) shrinks memory and boosts speed with little quality loss. Distillation and right-sizing — using the smallest model that passes your evals — often beat a bigger model on both cost and latency. Speculative decoding adds further speedups by drafting with a small model and verifying with the large one.
4. Route, Cache, and Tier
Not every request needs your largest model. Route simple queries to cheap models and hard ones to premium models. Cache exact and semantically similar responses. Stream tokens to cut perceived latency. Each layer removes load before it reaches the expensive path.
5. Autoscaling & Placement
GPU cold starts are slow and capacity is scarce, so scale on queue depth and time-to-first-token, keep warm pools for spikes, and place inference close to users. Track cost per thousand tokens and P95 latency as your core SLOs — and revisit them as traffic grows.
Fast, affordable inference is a stack of compounding optimizations: batch continuously, master the KV cache, run the smallest capable model, route and cache aggressively, and scale on the right signals. Together they turn an eye-watering GPU bill into a sustainable service.