HyperCache AI — Unified KV-Cache & Vector DB Acceleration SDK

Cut LLM KV-Cache VRAM by 58.2% (+30% faster decoding) AND compress Vector Database / RAG Embeddings by 57.3% (2.34x faster search) with zero model retraining.

👉 Get Your Instant Annual PRO License Key on Gumroad ($99/yr Launch Offer)

HyperCache AI is a dual-engine runtime acceleration SDK for PyTorch / Hugging Face LLMs (Qwen2.5, Llama-3, Mistral, Gemma) and Enterprise Vector Databases (Pinecone, Milvus, Qdrant, FAISS, pgvector):

1. Engine 1 — LLM KV-Cache Runtime Compressor: Preserves full-resolution semantic blocks while condensing historical background tokens on the fly, achieving 100% verbatim Needle-in-a-Haystack recall while cutting GPU KV-Cache memory in half.
2. Engine 2 — Vector DB & RAG Embedding Compressor: Compresses high-dimensional embedding vectors (e.g., 1536-D OpenAI / Cohere / BGE embeddings down to 656-D), cutting Vector Database RAM bills by 57.3% while preserving 100.0% Top-1 match accuracy and 99.92% cosine similarity correlation.

Verified Production Benchmarks

1. Live 3B & 7B LLM Needle-in-a-Haystack Retrieval (NVIDIA Tesla T4 — FP16)

Task: Retrieve a confidential vault authorization key ('HC-ENTERPRISE-9940-X') and reserve liquidity threshold (90.37 million USD) buried at token ~160 inside a 2,765-token cloud infrastructure report.

A) Qwen/Qwen2.5-3B-Instruct (100% Native GPU Resident on Tesla T4)

B) Qwen/Qwen2.5-7B-Instruct (Large-Scale VRAM Reduction)

• Uncompressed Baseline: 2,764 tokens (151.16 MB VRAM) $\to$ Retrieves 'HC-ENTERPRISE-9940-X' and 90.37 million USD.
• HyperCache AI SDK (-58.2% RAM): 1,156 tokens (63.22 MB VRAM) $\to$ Retrieves the exact same verbatim output while saving 87.94 MB VRAM per single request!

2. Vector Database & RAG Embedding Benchmark (5,000 Docs, 1536-D $\to$ 656-D)

3. Multi-Head Long-Context Attention Fidelity (N = 4,096 Tokens, 16 Heads)

Instant 30-Second Evaluation (Free Trial Key Included)

You can evaluate both engines right now on your own machine or in Google Colab using our public evaluation key:

• Public Evaluation Key: HYPERCACHE-FREE-TRIAL-2026 (Hardware-locked to 15 free evaluation compressions per machine, up to 4,096 tokens/dimensions per call).

Step 1: Python Environment & Requirements

Universal Python Version Support (3.8 – 3.13+): hypercache_sdk_client.py decrypts and compiles the runtime engine in volatile RAM directly on your target Python interpreter, ensuring seamless compatibility across Python 3.10, 3.11, 3.12, and 3.13 (including Google Colab, Kaggle, and production Linux/Windows servers).

pip install numpy cryptography torch transformers accelerate

Step 2: Run the Unified Quickstart Benchmark (KV-Cache + Vector DB)

Download hypercache_sdk_client.py and quickstart_demo.py into the same directory and run:

python3 quickstart_demo.py

Step 3: Use Both Engines in Python (3 Lines of Code)

import hypercache_sdk_client

# 1. Unlock the AES-256-GCM dual engine in RAM
engine = hypercache_sdk_client.load_hypercache_engine(
    license_key="HYPERCACHE-FREE-TRIAL-2026"
)

# 2A. Compress HuggingFace LLM KV-Cache on the fly (58.2% VRAM saved)
compressed_kv = engine.compress_torch_cache(
    past_key_values, _inp_ids=input_ids, _mdl=model, _q_len=48
)

# 2B. Compress Vector DB / RAG Document & Query Embeddings (1536-D -> 656-D, 57.3% RAM saved)
compressed_docs    = engine.fit_compress_embeddings(doc_embeddings_1536d)
compressed_queries = engine.compress_query_embeddings(query_embeddings_1536d)

Annual Licensing, Early-Bird Pricing & Seat Management

HyperCache AI is distributed as an AES-256-GCM authenticated commercial vault (hypercache_sdk_client.py) that decrypts strictly in volatile RAM upon license verification.

👉 Click Here to Purchase Your Instant Annual PRO License Key on Gumroad

Moving Your PRO License Between Machines

Both Developer PRO (2 Seats) and Enterprise Cluster PRO (10 Seats) allow you to transfer active seats between workstations and cloud servers at any time with a single line of Python:

# Frees the current machine seat immediately so you can activate on a new server:
print(engine.deactivate_current_machine())

(For dynamic Kubernetes / Docker auto-scaling environments where pods spin up and down continuously, choose Enterprise Unlimited (K8s) for zero seat-tracking overhead).