Chunk-Level Cache Reuse Delivers 48x Faster LLM Inference on Standard Hardware

KVBoost solves a fundamental inefficiency in LLM inference: system prompts are recomputed from scratch on every request, wasting GPU cycles and VRAM. By introducing chunk-level KV cache reuse with FlashAttention-2 optimization, it achieves 3–5x faster time-to-first-token while slashing VRAM requirements from 60+ GB to 8 GB. This no-code integration with HuggingFace enables enterprise-scale LLM deployment on consumer-grade GPUs without model rewrites.