Cut PyTorch / Hugging Face 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.
Use the sliders below to calculate your exact Vector Database index RAM reduction, alongside our verified benchmark metrics measured on 5,000 documents (1536-D → 656-D).
| Vector DB Compression Method (Verified 5,000-Doc Benchmark) | Compressed Dim | RAM Saved | Verified Top-1 Match (↑) | Verified Recall@10 (↑) | Verified Cosine Correlation (↑) |
|---|
Calculate your GPU KV-Cache VRAM savings for any context length and batch size, alongside our verified results tested on NVIDIA Tesla T4 (FP16) with Qwen/Qwen2.5-3B-Instruct and Qwen/Qwen2.5-7B-Instruct.
| KV-Cache Method (Verified Tesla T4 Benchmark + Calculated VRAM) | Cached Tokens | Estimated KV VRAM | Verified T4 Speed | Needle Fact Retrieved? | Verified Model Output on NVIDIA Tesla T4 |
|---|---|---|---|---|---|
| 1. Uncompressed Baseline (100% RAM) | 2,764 | 151.16 MB | 15.2 tok/s | Yes (100%) | The primary vault authorization key is 'HC-ENTERPRISE-9940-X' and the reserve liquidity threshold is 90.37 million USD |
| 2. HyperCache AI SDK (-58.2% VRAM) | 1,156 | 63.22 MB | 19.7 tok/s (+30%) | Yes (100% Verbatim) | The primary vault authorization key is 'HC-ENTERPRISE-9940-X' and the reserve liquidity threshold is 90.37 million USD |
| 3. Standard Sliding Window (-58.2% VRAM) | 1,156 | 63.22 MB | 19.1 tok/s | Failed (0%) | The primary vault authorization key is the exact primary vault authorization key... (Loops & Hallucinates) |
Estimate how much cloud RAM and GPU VRAM budget your team saves annually with HyperCache AI.
Install the actual AES-256-GCM Python SDK directly from Official PyPI and test on your own models or in Google Colab using our public evaluation key (HYPERCACHE-FREE-TRIAL-2026):
pip install hypercache-ai torch transformers accelerate
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, +30% faster decoding)
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)
| Feature | Free Evaluation Trial | Developer / Startup PRO | Enterprise Cluster PRO | Enterprise Unlimited (K8s) |
|---|---|---|---|---|
| Annual Price (Launch Offer) | Free | |||
| Active Machine Seats | 1 Machine (Non-Transferable) | 2 Transferable Seats | 10 Transferable Server Seats | Unlimited Nodes / Containers |
| Total Runs Allowed | 15 Runs (Hardware-Locked) | Unlimited (24/7 Production) | Unlimited (24/7 Production) | Unlimited (24/7 Production) |
| Max Context / Vector DB Size | Up to 4,096 Tokens / Dims | Unlimited (32k–1M+ Tokens) | Unlimited (32k–1M+ Tokens) | Unlimited (Cluster-Wide) |
| Instant Checkout | HYPERCACHE-FREE-TRIAL-2026 |
Buy Developer PRO ($99/yr) → | Buy Cluster PRO ($399/yr) → | Buy Unlimited K8s ($1,499/yr) → |