Show HN: KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT (pythongiant.github.io)

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

Bad site design, if I can't scroll to see the next slide, that's just broken.

The functionality is impressive, but the website needs some work

I just dont get why people choose Python and not e.g. Go for high performance problems.

Is this based on paged attention with hashing of the pages?

Here's the repository incase anyone wants to have a look at the code. leave a star if you find it interesting :P https://github.com/pythongiant/KVBoost

Drop in replacement for what exactly? Can I use it with llama.cpp and Vulkan? Or vLLM and ROCm?