KVBoost — Pitch Deck

pip install kvboostKVBoostFaster LLM Inference.Less VRAM. No Model Changes.Chunk-level KV cache reuse  ·  FlashAttention-2  ·  AWQ layer streaming  ·  CPU paged decoding⚡The ProblemLLM inference is broken by default.🧱VRAM WallsModern LLMs like Qwen2.5-32B require 60+ GB VRAM at full precision — out of reach for most teams.🐢Slow PrefillRepeated system prompts are recomputed from scratch on every single request — wasting GPU cycles constantly.🔧HF BottlenecksHuggingFace's default inference loop has no KV cache reuse, no chunked attention, and no memory-efficient decoding.The SolutionKVBoost: drop-in, no rewrites.from kvboost import KVBoost engine = KVBoost.from_pretrained(   "Qwen/Qwen2.5-3B" ) # Warm a shared prefix once engine.warm("You are a helpful assistant...") # All subsequent calls reuse cache result = engine.generate(prompt) print(result.kv_reuse_ratio)  # ✓ 80%+⚡KV Cache ReuseChunk-level cache reuse eliminates redundant prefill for shared prompts.🚀FlashAttention-2Memory-efficient attention with 3–5× TTFT speedup vs vanilla HuggingFace.💾AWQ Layer StreamingRun 32B+ models on 8 GB VRAM via pinned-host weight streaming.🗄CPU Paged DecodingSpill KV cache to CPU RAM — handle long contexts without OOM errors.

PerformanceReal numbers. Real hardware.3–5×TTFT Speedupvs HF Baseline80%+KV Cache Hit RateMulti-Turn8 GBVRAM for 32B ModelAWQ Streaming~10KLines of Code43 Python ModulesTime to First Token (ms) — lower is betterHF Baseline850msPrefix Reuse320msChunk Reuse210msMulti-Turn Cache Hit Rate (%)Turn 10%Turn 245%Turn 368%Turn 478%Turn 5+85%How It WorksFour layers of optimization.01Hash ChunksIncoming prompt is split into chunks. Each chunk is hashed to look up prior cached K/V pairs.02Reuse CacheMatching chunks skip attention entirely. Only novel tokens are forwarded through the transformer.03Flash AttentionNew tokens run FlashAttention-2 — tiled CUDA kernels with O(√N) memory. No custom model code needed.04Page OffloadLong-context KV blocks are evicted to CPU RAM via async DMA — enabling contexts beyond GPU VRAM.AWQ Layer StreamingRun a 32B model on a gaming GPU.$ python -m kvboost.streaming.demo_partial_8b   --model Qwen/Qwen2.5-32B-Instruct-AWQ INFO: Replaced projections:   56 resident across 8 layers   392 streamed across 56 layers   load_time: 10.7s   peak_vram_after_load: 5.65 GB   avg_tok_per_s: 0.11   peak_vram_during_decode: 6.13 GB5.65 GBPeak VRAM after loading a 32B model — fits on a single 8 GB gaming GPU.6.13 GBPeak VRAM during decode — stays safely under the 8 GB limit.0.11 tok/sPCIe-bound throughput — built for VRAM savings, not raw speed.Use CasesWho needs KVBoost?💻AI Coding AssistantsSystem prompts are re-used across 100s of requests.

Cache the context once, speed up every response by 3–5×.📚RAG PipelinesDocument chunks appear in many queries. Chunk-level reuse makes multi-document QA dramatically faster.⚙Edge / Budget InfraAWQ streaming lets teams deploy 30B+ models on consumer GPUs — no $10K A100 required.💬Multi-Turn ChatbotsConversation history grows each turn. CPU paged decoding handles long contexts without OOM crashes. MIT Licensed  ·  Drop-in with HuggingFace Transformers  ·  No fine-tuning, no architecture changes TechnologyBuilt on solid foundations.✓FlashAttention-2Tiled CUDA kernels for O(√N) memory attention✓AWQ (AutoQuant)Weight-only 4-bit quantization preserving accuracy✓HuggingFace TransformersDrop-in compatibility — no model changes required✓CUDA DMA StreamsAsync PCIe transfers for layer-by-layer weight streaming✓Chunk HashingDeterministic token-level hashing for cache lookup✓CPU Paged MemoryPage-table KV offload — evict cold blocks to RAM✓PyPI Packagepip install kvboost — ready in 2 minutes✓MIT LicenseFully open source, production-ready for any useRoadmapWhat's next.Now ✅✓  Chunk-level KV reuse✓  FlashAttention-2 integration✓  AWQ layer streaming✓  CPU paged decodingNext 🔨◦  Multi-GPU tensor parallel◦  Speculative decoding◦  LoRA adapter hot-swap◦  Continuous batchingFuture 🔭◦  GGUF / GGML support◦  Triton custom kernels◦  Distributed KV cache◦  Cloud-hosted cache tierStart buildingfaster. KVBoost is open source and production-ready.Drop it into any HuggingFace project today. GitHubgithub.com/pythongiant/kvboostPyPIpypi.org/project/kvboost/Docskvboost.readthedocs.io$ pip install kvboost MIT License · Built by @pythongiant1 / 10