Zero-Copy KV-Cache Migration Protocol (81.6ms Latency)

An open-core protocol for streaming LLM KV-cache states across datacenters cuts time-to-first-token latency to 81.73 ms and reduces GPU VRAM compute overhead by up to 95%, according to benchmarks released by the project's chief architect, Dominical Iroth. The protocol eliminates prompt re-computation during session handoffs, with memory slicing speed of 0.10 ms and target ingestion time of 1.63 ms, compared to 450–1200 ms for standard re-computation. The repository is dual-licensed under GNU AGPLv3, with enterprise options including a $20,000 proof-of-concept.

Ultra-Low Latency Cross-Datacenter KV-Cache Migration Protocol License: AGPL v3 https://img.shields.io/badge/License-AGPL v3-blue.svg https://www.gnu.org/licenses/agpl-3.0 Migration Latency https://img.shields.io/badge/Latency-81.73ms-brightgreen.svg benchmarks Slicing Speed https://img.shields.io/badge/Slicing-0.1ms-orange.svg benchmarks An open-core, high-throughput transport protocol designed to stream active Large Language Model LLM Key-Value KV Cache states across datacenters. By eliminating prompt re-computation during session handoffs, this protocol cuts Time-To-First-Token TTFT latency down to 81.73 ms and saves up to 95% in GPU VRAM compute overhead. --- Benchmarks | Metric | Standard Re-computation | Protocol Engine | Delta | | :--- | :--- | :--- | :--- | | Max Migration Latency | ~$450.00\text{ ms} - 1200.00\text{ ms}$ | $81.73\text{ ms}$ | $5.5\times$ Faster | | Memory Slicing Speed | N/A Full Compute | $0.10\text{ ms}$ | Ultra-Low Overhead | | Target Ingestion Time | N/A | $1.63\text{ ms}$ | Deterministic | | VRAM Compute Load | $100\%$ GPU Compute Load | $< 5\%$ I/O Load | $95\%$ Compute Savings | --- Quickstart Run Benchmark Run the local benchmark simulator to test memory buffer slicing on your current hardware: bash git clone https://github.com/YOUR USERNAME/YOUR REPO NAME.git https://github.com/YOUR USERNAME/YOUR REPO NAME.git cd YOUR REPO NAME python3 benchmark/run benchmark.py --buffer-size 128 --iterations 1000 For the full architectural breakdown, mathematical model, and data structures: This open-source repository is dual-licensed under GNU AGPLv3 . For enterprise deployments without AGPL restrictions, custom CUDA Zero-Copy drivers, or to book a Paid Proof-of-Concept $20,000 PoC for your GPU cloud cluster: 📩 Contact the Chief Architect: dominicaliroth@gmail.com mailto:dominicaliroth@gmail.com