KVBoost significantly improves the performance of HuggingFace models by accelerating inference and reducing VRAM usage.

KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT https://pythongiant.github.io/KVBoost/ #HackerNews #KVBoost #HuggingFace #AI #Performance #Optimization #CacheReuse #TTFT

Show HN: KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT - https://pythongiant.github.io/KVBoost/ #hackernews

Show HN: KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT ---- - an hour ago | 8 points | 4 comments - URL: https://pythongiant.github.io/KVBoost/ - Discussions: https://news.ycombinator.com/item?id=48232060 - Summary: KVBoost is an open-source Python tool that accelerates LLM inference and reduces VRAM usage without modifying models. It integrates with HuggingFace Transformers to address high memory costs, slow prefill from repeated prompts, and inefficient default inference loops. Core optimizations include chunk-level KV cache reuse, FlashAttention-2, AWQ layer streaming, and CPU paged decoding. This delivers 3–5× faster time-to-first-token versus the HuggingFace baseline, over 80% cache hit rates for multi-turn conversations, and the ability to run 32B models on 8 GB GPUs. Prompts are hashed into chunks, allowing prior key/value pairs to skip redundant computation; new tokens use memory-efficient attention, and long contexts spill to CPU RAM via async DMA. Key use cases are coding assistants, RAG pipelines, budget edge deployment, and multi-turn chatbots. The package is MIT-licensed, installable via PyPI, and requires no fine-tuning. The roadmap includes multi-GPU tensor parallelism, speculative decoding, and continuous batching.