KV Cache, Quantization, and Latency Tricks for Small-Scale AI Inference

Most inference optimization advice targets large GPU clusters, leaving developers on single-GPU or modest cloud setups without practical guidance. A technical breakdown addresses three key levers available at small-to-mid scale: KV cache management, quantization, and their associated latency tradeoffs. The KV cache, which stores key and value tensors to avoid recomputing attention at every generation step, often consumes more memory than model weights themselves when serving multiple concurrent users with long contexts. Quantization reduces model weight precision — from fp16 down to int4 or GGUF formats — shrinking memory footprint and improving latency at some quality cost. Techniques like grouped-query attention, sliding window attention, and PagedAttention-style memory management can further reduce overhead without requiring additional hardware.