KVBoost is a chunk-level KV cache reuse library for HuggingFace models (pip install kvboost). It supports two recompute strategies (selective boundary and Cache

KVBoost is a chunk-level KV cache reuse library for HuggingFace models (pip install kvboost). It supports two recompute strategies (selective boundary and CacheBlend), int8/int4 KV quantization for 2–4x RAM reduction, disk-backed cold storage, and 11 architectures including Llama, Qwen, Gemma, Mistral, and Phi. On Qwen2.5-3B we measured 47.9x TTFT speedup on an 8-turn conversation, 21x on code context reuse, 100–743x faster than MLX, and 3–41x faster than vLLM-MLX — including interior chunk reuse where vLLM gets zero hits. Outputs are token-for-token identical to baseline under greedy decoding. Works best on 3B+ models with 500+ token shared context. GitHub: https://github.com/pythongiant/KVBoost https://github.com/pythongiant/KVBoost